A Guide to the New Addenda to AMCP Format Version 3.1 PG5 Should We Be Investing More in Mental Health Products? The Impact of Experience-based Utility Measures on Reimbursement Decisions PG9 Models Do Drive Decision Making – But Do They Take You Where You Want to Go? PG12 EVIDENCE MATTERS ISPOR PRESENTATIONS PG23 UPCOMING PRESENTATIONS PG30 RECENT PUBLICATIONS PG28 INNOVATIVE APPROACHES TO DATABASE STUDY DESIGN AND ANALYSIS David Neasham, PhD, MSc, MFPH, EU Director and Senior Research Scientist, Epidemiology and Database Analytics Andrew Cox, PhD, Senior Research Associate, Health Economics PG1 APRIL 2013
Transcript
A Guide to the New Addenda to AMCP Format Version 3.1 PG5
Should We Be Investing More in Mental Health Products? The Impact of Experience-based Utility Measures on Reimbursement Decisions PG9
Models Do Drive Decision Making – But Do They Take You Where You Want to Go? PG12
INNOVATIVE APPROACHES TO DATABASE STUDY DESIGN AND ANALYSISDavid Neasham, PhD, MSc, MFPH,
EU Director and Senior Research Scientist, Epidemiology and Database Analytics Andrew Cox, PhD,
Senior Research Associate, Health Economics PG1
APR
IL 2
013
Cover Story:Innovative Approaches to Database Study Design and Analysis: Opportunities and Challenges 1
A Guide to the New Addenda to AMCP Format Version 3.1 5
What the AMCP Version 3.1 Format for Formulary Submissions Means to Drug Manufacturers 8
Should We Be Investing More in Mental Health Products? The Impact of Experience-based Utility Measures on Reimbursement Decisions 9
Models Do Drive Decision Making – But Do They Take You Where You Want to Go? 12
The RE-ADAPT Study: Will it Prove to be a Proof of Concept? 14
Incorporating the Payer Perspective into Clinical Development Programs 17
Rasch Model Analysis and Small Sample Size 19
ISPOR Presentations 23
Recent Presentations 26
Recent Publications 28
Upcoming Presentations 30
Contents
EVIDENCE MATTERS APRIL 2013
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HealtH economics, outcomes ReseaRcH, maRket access, Data analytics, epiDemiology
David Neasham, PhD, MSc, MFPH
Andrew Cox, PhD
Innovative Approaches to Database Study Design and Analysis: Opportunities and Challenges
David Neasham, PhD, MSc, MFPH, European Director and Senior Research Scientist, Epidemiology and Database Analytics, and Andrew Cox, PhD, Senior Research Associate, Health Economics
Navigating medical products successfully to market and beyond is highly complex –
compounded by the fact that understanding the needs and criteria of stakeholders
(whether regulatory, payer, patient or prescriber) often requires substantial
effort and strategic thinking. Anticipating stakeholder needs for effective planning
allows for evidence to be produced that will be of value to stakeholders.
With the changing regulatory and payer environment, what are the implications for database study design and analysis in the future?Typically, electronic healthcare database (eHCD) studies have been undertaken as additional studies within the framework of Risk Manage-ment Plans (RMPs) and Risk Evaluation and Mitigation Strategies (REMS). However, eHCDs are now being used in comparative-effectiveness research for payers and also earlier in drug development to investigate issues such as treat-ment switching patterns and patient profiling. (See Figure 1.) Additionally, the emergence of the new benefit-risk requirements, for example within the framework of the EU Pharmaco-vigilance Regulation 1235/2010 and Directive
2010/84/EC (legislation active since January 2013), has brought renewed focus on the need for well designed, post-authorization database studies in Europe.1
Benefit-risk assessment (BRA) requirements are needed both from a regulatory and a payer perspective, an indication that the data requirements for both groups are beginning to converge to some extent. From a payer perspective, although the mainstay of such BRAs rely on efficacy data from clinical trials, there is an increasing drive to assess whether an intervention does more good than harm when provided under conditions of routine care (since many randomized efficacy trials often exclude patient populations that may use the treatment most, such as older patients and patients with multiple morbidities). (See Figure 2.)Figure 1
Current Regulatory & Payer Landscape: eHCD needs
FDA requirements
EMA requirements
Early drug development
Phase III drug development
Regulatory submissions
Lice
nce
Laun
ch
Product promotion
RMPs/REMS
US value demonstration
EU/national value demonstration
pre-authorisation
eHCD studies have typically taken place within the framework of RMPs/REMS additional studies. However, increasingly, eHCDs are being used in comparative-effectiveness research for payers and earlier in drug development
to investigate issues such as treatment switching patterns and patient profiling.
Safety surveillance
payersRegulators Benefit: risk
requirements
EU/national value demonstration
post-authorisation
Additional studies
EU = Europe; EMA: European Medicines Agency; FDA: Food and Drug Administration; US = United States; eHCD = Electronic Health Care Database; RMP: Risk Management Plan; REMS: Risk Evaluation and Mitigation Strategies
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Selecting the most appropriate data sources, research design and analytic strategy to address a CER question that will inform specific healthcare decisions requires the alignment of numerous perspectives. Where a gap may exist between the capabilities of a data source and the CER question, emerging methods for CER and patient-centered outcomes research are now utilizing (where possible) clinically rich data sources or enrichment through linked supple-mental data to fill the gap. Significant challenges remain, however. In particular, CER evidence is often most critical shortly after a new therapy is introduced into the market, and it is in this early marketing phase where channeling of patients to the new therapy may be particularly strong and where information on users is still sparse, a combination that is particularly challenging. Propensity score matching methods are best employed in these circumstances to reduce potential bias due to confounding.
From a regulatory perspective, signal detec-tion data collection in the pre-approval phase also brings certain challenges. Samples are often narrowly defined and therefore may not be truly representative of the final treatment population,
and short timeframes within the pre-approval phase and/or insufficient sample sizes may result in inadequate statistical power to evaluate safety issues properly. Post-marketing spontaneous reporting methods, while very valuable first line of defense systems, have equally challenging limitations. There may be under-reporting or over-reporting (the latter particularly problematic in the early post-launch period), and reporting is “self-reporting” (i.e., people are self-selected into the reporting system and may not be representa-tive of the treatment population at large). Indeed, this may cause false positive reporting due to bias and small sample sizes. In addition, there is limited population-level denominator informa-tion, making the provision of incidence estimates on safety outcomes difficult to attain, with many underlying assumptions. It is within this context that very large validated medical record databases, with systematic data collection over long periods of time, become relevant. Denomi-nator data are available, incidence of potential safety events can be evaluated in the general population, studies can be sufficiently powered, self-reporting bias is limited, and both rare and common safety events can be monitored.
Knowing that observational database studies are going to be required, it would seem logical to design an integrated strategy that addresses payer needs, including demonstrating added value to the payer and differentiating the product from competitors, as well as addressing regula-tory needs, such as compliance with Committee on Human Medicinal Products (CHMP) requests for safety information and/or concerns over potential drug utilisation patterns. Designing studies to define research questions, review data sources, and develop protocols for analysis that address both payer and regulator needs can be both time efficient and cost-effective.
Figure adapted from: Comparative-effectiveness research (CER), Goal of CER in contrast to preapproval randomised controlled trials (RCTs).2
Efficacy (can it work?)Effectiveness (does it work in routine care?)
Placebo comparison (for usual
care)
Most RCTsfor drug approval
Goal of comparative-effectiveness ratio
(CER)
Active comparison
(head-to-head)
PROsn Often the eHCD sources are what
the authorities are asking forn Large representative real-world
populationsn Longitudinal individual level
1° or 2° care data, linkablen Potential for innovative approachesn Can be cost-effective/economies of
scale combining needs
CONsn Data quality •Understand the limitations •Need expertise in understanding how
the data are structured, linked • Level of clinical information may be
requiredn Privacy and patient confidentiality issues
need to be consideredn Access and expertise required
Data challenges from Biopharmaceutical’s perspective?
Figure 2
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Database Study Programs in EuropeIn designing a successful database study program, the first step should be to clearly identify the research questions needed. Once this has been achieved, data source options can be reviewed and decisions made on the best approach to answer the specific needs identi-fied. Lastly, research questions, methodology and databases should be reviewed in order to develop core protocols to address both payer and safety perspectives. Figure 3 outlines this process.
Hybrid Study DesignsWith the number of different data sources available, a hybrid approach to data gathering should also be considered. This is often over-looked; however, before implementing a costly study design, such as a registry, it is a good idea to review requirements in terms of what data already exist in eHCDs and possible links that could provide the information needed, such as
registers, physician text comments, de novo data collection, cohort studies, etc. (See Figure 4.)
There are many advantages of this kind of combinational or hybrid approach, such as:
n The database can be used as a sample frame from which patients can be identified and recruited de novo, a very time and resource efficient method
n Additional clinical data can be garneredn Cases can be validatedn Outcome measures can be identifiedn Key requirements of regulator/payer can be
addressedn Extra granularity of data can allow better
matching of cases and controls
Alternative Approaches to Database Study DesignThe traditional approach to study design is one with which we are most familiar, where a study is designed a priori with the single purpose of collecting data to answer a specific question. Common examples are laboratory experiments, clinical trials and field studies.
However, the amount of retrospective observa-tional data that is available is increasing dramati-cally, both in terms of the quantity and quality of the data and the number of different sources. These data are not collected with a specific and focused purpose, although they can be used to answer specific questions (often in a more cost-effective manner). Examples of this type of data in current use are electronic medical records (Clinical Practice Research Datalink[CPRD] and The Health Improvement Network [THIN]) and claims data in the United States. The analytical approach to these “real life” data sources is fundamentally different. Such data sources are often large in size, and are characterized by the fact that, for a particular focused research question, the uninformative data to useful data ratio is very high. One alternative approach to this kind of situation is that of data mining or knowledge discovery, a kind of exploratory analytical approach. It allows patterns in the data to be discovered, is hypothesis free, and is in fact hypothesis generating.
This approach is best illustrated with a simple example with data from the Clinical Practice Research Database (CPRD), widely used in evidence generation to characterize epide-
Define research question Pilot study(s)
Review data source options
Develop protocol(s)
Define clear research question(s) for assessing comparative -effectiveness/safety of product vs. comparator treatments
Include:A critical appraisal of effectiveness outcome measures and methodologies across the literature
A qualitative and/or quantitative assessment of specific outcome definition(s)
Hypotheses on key points of differentiation of product vs. comparators
Conduct pilot study(s) in suitable markets to test the analytical plan (CER)
Assess the availability of electronic healthcare databases in the EMA region and their suitability for conducting CER and/ or post-authorisationsafety (PAS) studies
Develop a view onthe desirability of conducting such studies in these databases
Identify pilot study opportunities
Identify any important gaps in database availability and develop action plans
Combine the results from the research question, methodology review, and database evaluation to develop core protocols:(i) CER; (ii) PAS study
Develop full EMEA database study
programme
PAS study
Figure 4
Figure 3
Study designs: Hybrid database-case report forms (CRF) studies
Levels of data according to study needs: eHCD alone eHCD + linkage to register(s) eHCD + physician text comments eHCD + de novo data collection CRF or electronic CRF (eCRF) eHCD + combinations of ii-iv e-Registers (indication-specific) Cohort studies + linkage to eHCD
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miological and economic aspects of a given condition. To illustrate a data mining (explor-atory) approach, we can collect the data which describes the treatment histories of patients with psoriatic arthritis from point of diagnosis (as an example). This approach will allow any patterns present in the data to be revealed; discovered patterns can then be further inves-tigated with the aim of determining if they are of clinical or epidemiological importance. If the discovered patterns turn out to be relevant and interesting, analysis is then possible with more traditional statistical approaches. In the case of psoriatic arthritis, a technique was applied which is capable of showing if the treatment histories of patients fall into specific groups or clusters. We found there were three strong and clear groupings in the treatment histories of psoriatic arthritis patients, with one group in particular having a much more unsettled and changeable treatment history than the other two groups. (See Figure 5.) Having uncovered this pattern, the next task is to investigate the underlying reasons. The reasons could simply confirm what may already be known to clinicians, could reflect adherence (or not) to existing treatment guide-
lines, or could highlight a group that is not well served by currently available treatment options. It could also, with the aid of additional tech-niques, help predict, a priori, which patients are likely to have changeable treatment histories.
Although this is just a small example of one particular data mining approach, it serves to illustrate some key features necessary or impor-tant for using a data mining approach.
n Capable of handling extremely large datasetsn Useful for extremely complex datan Not constrained by many of the assumptions
of traditional statistical methodsn Able to reveal patterns in the datan Largely hypothesis free and can generate
hypothesis for later statistical analysisn Very useful where quantity of uninformative
data is high relative to useful datan Has an outstanding track record in other
industriesn Predictive capabilities can be formally tested
Data mining is perhaps an underused alterna-tive approach in generating evidence in the health care industry, even though it has wide-spread and successful use in other areas. It is an approach frequently applied in banking, fraud detection, market segmentation, online security, drug discovery, and, of course, in the recent discovery of the Higgs Boson at CERN.
Database studies will always be a key factor in providing necessary information for both regulators and payers. As the needs of those stakeholders increasingly overlap, looking at study development in a strategic way and being open to new and innovative approaches to data becomes more and more important. The power of data is overwhelming, and in the growing “electronic age” that continually makes data more accessible, the way we look at and analyze data needs to also evolve to take advantage of its full potential.
References 1 International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use:
Periodic Benefit-Risk Evaluation Report E2C (R2). Available at http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E2C/E2C_R2_Step4.pdf. Accessed 2013 April 1.
2 Schneeweiss S, Gagne JJ, Glynn RJ, Ruhl M, Rassen JA. Assessing the Comparative Effectiveness of Newly Marketed Medica-tions: Methodological Challenges and Implications for Drug Development. Clin Pharmacol Ther. 2011 Dec; 90(6):777-790. [Epub 2011 Nov 2]
Figure 5: Results of applying a cluster detection technique to data detailing the treatment histories for psoriatic arthritis patients (PsA) and psoriasis patients (PsO).This pattern was not evident in psoriasis (PsO) patient data.
HealtH economics, outcomes ReseaRcH, maRket access, Data analytics, epiDemiology
Addenda issued recently from the Academy of Managed Care Pharmacy (AMCP), as part of the latest update to AMCP Dossier Format for Formulary Submissions (Version 3.1), direct the development of dossiers for companion diagnostics and specialty pharmaceuticals.1 In addition, the new addenda guide manu-facturers regarding the use of comparative effectiveness research in dossiers. These addenda do not supersede the existing Format; rather, they extend its guidance in these areas to offer more specific recommendations to manufacturers. The goal is to better inform payers making coverage and reimbursement decisions regarding companion diagnostics, drugs used alongside companion diagnostics, specialty pharmaceuticals, and any product for which the payer will be interpreting compara-tive effectiveness research.
1. Companion Diagnostic Tests (CDTs)This addendum provides guidance for the development of dossiers on companion diag-nostic tests (CDTs) and drugs used in conjunc-tion with CDTs. In this addendum, CDTs are defined as “tests that provide information that improves the safety or effectiveness of a drug or biologic.” CDTs may have been developed alongside the drug, with approval by the Food and Drug Administration (FDA) granted to both simultaneously, or the drug and its test may have been developed separately; the test may indeed have been designed for use with the drug class, and not the specific product. The addendum notes that the responsibility for developing a dossier may be complicated if the drug and its test do not share the same manufacturer. Thus, the addendum organizes part of its guidance for dossier development by this history of product development:
n When the CDT and drug (or biologic) were developed together, the data for both should be included within the drug’s dossier. This includes analytical validation of the CDT, whether or not it was validated in the drug’s clinical trials.
n When the CDT and drug were developed separately, and the drug’s label requires the test, all of the information about the test (including validation studies, if feasible) should go into the drug’s dossier. If the drug’s label does not require the test, the test’s manufacturer should submit a separate dossier.
n When the CDT and drug were developed separately and the CDT was designed for the drug class and not the specific product, the test’s manufacturer should submit a separate dossier.
For dossier sections providing evidence relating to the CDT, the addendum recommends particular categories of information specified by the Agency for Healthcare Research and Quality (AHRQ)2:
n Test performance according to technical specifications
n How well the test classifies patients into clinically meaningful categories
n Impact of the test on clinical management and patient outcomes
n The benefits of the test to society, including incremental cost effectiveness
Additional information should be included in section 2.3 of dossiers that are designed for CDTs alone, in cases where the drug has a separate dossier. Categories of information to provide in this section are listed, including the approved indications, the clinical basis for the test, the prevalence of the CDT variants in the target population, and a number of other items important for determining how the test fits into current therapeutic approaches.
2. Specialty PharmaceuticalsThe addendum for specialty pharmaceuticals offers guidance for creating dossiers for these products, which are becoming an increasingly important segment of the U.S. healthcare system. Specialty pharmaceuticals have not
The addendum for specialty pharmaceuticals offers guidance for creating dossiers for these products, which are becoming an increasingly important segment of the U.S. healthcare system.
A Guide to the New Addenda to AMCP Format Version 3.1
Talia Foster, MS, Senior Research Scientist and Senior Director, Evidence Review and Synthesis
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always had a standard definition,3-5 but are defined in the addendum as products which “require 1) a difficult or unusual process of delivery to the patient (preparation, handling, storage, inventory, distribution, Risk Evalua-tion and Mitigation Strategy [REMS] programs, data collection, or administration), or 2) patient management prior to or following administra-tion (monitoring, disease or therapeutic support systems).4 While high cost is frequently associ-ated with specialty pharmaceuticals, it was not used as a component of the definition for use within the Format, as individual payers will define their own cost criteria used for desig-nating products as specialty pharmaceuticals.
Extensions of the Format for specialty pharma-ceuticals include the following:
n Because some products may be covered under the medical or pharmacy benefit, the addendum requires additional codes for these products if they are relevant to reim-bursement, including Healthcare Common Procedure Coding System (HCPCS) codes,6,7 Current Procedural Terminology (CPT) codes,8 and International Classification of Diseases (ICD-9) codes.9
n Instructions for special handling, preparation, administration, and care settings that do not appear in the package insert should be incorporated into section 2.
n The section on access restrictions should be expanded up to five paragraphs to cover considerations for the product around distribution channels, prescribing restrictions, instructions for handling and ordering, and access assistance if available.
n Sections detailing economic impact should include additional costs beyond the cost of the drug, such as those related to special handling, delivery, REMS programs, and other administrative processes.
n Because many specialty pharmaceuticals may be first-in-class products without clear comparators, the table that compares the product’s package insert information to that of its primary comparators can be replaced with a short text description of the new technology.
n The disease treated by a specialty product may be unfamiliar to readers, so the disease description in section 2 can be expanded from two to three pages to provide more detail.
n Some specialty pharmaceuticals may be accompanied by more than one ancillary disease or care management program, such as educational services, nursing support, adherence promotion programs, REMS, access assistance, etc. The page limit of section 2.2.2, Approaches to Treatment, can be expanded by up to one page per indica-tion for products accompanied by multiple such programs.
n Models that allow users to enter their own numbers for costs, epidemiology, and other inputs are ideal, as these items will change among health care organizations.
n Products that are biosimilars will require their own dossiers like those of the innovator products. The addendum defines biosimilars according to the FDA as those “highly similar to the reference product notwithstanding minor differences in clinically inactive components” with “no clinically meaningful differences between the biological product and the reference product in terms of safety, purity and potency.”10
3 Comparative Effectiveness ResearchWhile the addenda on companion diagnostic tests and specialty pharmaceuticals provide specific instructions regarding use of the Format to create dossiers for these products, the addendum on comparative effectiveness research (CER) does not address the design of dossiers. This is because the Format has always envisioned that all available CER evidence would be included; however, it has not and will not specifically be required. Thus, this particular addendum is provided to be informational only. For example, the addendum notes that “CER emphasizes the comparison of alterna-tive health care interventions within typical care settings with a special focus on what health-care decision-makers such as patients and health plans care about” [emphasis original]. In other words, CER tries to assess whether interventions work in the real world, compared
“CER emphasizes the comparison of alternative health care interventions within typical care settings with a special focus on what healthcare decision-makers such as patients and health plans care about”
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to current interventions (effectiveness: “does it work?”) versus the controlled settings of clinical trials (efficacy: “can it work?”), often compared to placebo. The addendum provides an overview of several key types of CER study designs for potential citation within a dossier:
n Bayesian and adaptive trial designs, randomized trials that can change dynami-cally with the availability of new relevant evidence
n Pragmatic clinical trials, randomized trials designed to more accurately reflect real-world circumstances to promote generaliz-ability of their findings
n Prospective observational studies, nonrandomized, non-interventional designs that collect real-world data prespecified in a protocol
n Retrospective observational studies, nonrandomized, non-interventional designs that use secondary data sources from large populations, often followed longitudinally
n Systematic evidence reviews, analytical syntheses of all available studies of a given research topic that meet prespecified criteria; meta-analyses can be done with sufficient homogeneity
n Modeling studies, algorithms intended to represent a disease or clinical process, yielding estimates of patient or economic outcomes
The AMCP Format was designed to be flex-ible in meeting information needs for a wide variety of products. Changes in the delivery of healthcare, the types of products available, and technology assessment have raised questions by users of the Format seeking more guid-ance in these situations. The new addenda are intended to keep the Format a “living docu-ment” that can change dynamically with these shifts in healthcare.
References 1 The AMCP Format for Formulary Submissions Version 3.1: A Format for Submission of Clinical and Economic Evidence
of Pharmaceuticals in Support of Formulary Consideration. December 2012. Accessible at: http://amcp.org/WorkArea/DownloadAsset.aspx?id=16209.
2 Agency for Healthcare Research and Quality. Methods Guide for Medical Test Reviews. AHRQ Publication No. 12-EC017. June 2012. Rockville, MD. Accessible at: http://effectivehealthcare.ahrq.gov/ehc/products/246/558/Methods-Guide-for-Medical-Test-Reviews_Full-Guide_20120530.pdf Accessed 2013 March 13.
3 Foundation for Managed Care Pharmacy. Specialty Pharmacy Initiative: Phase I Discovery & U.S. Environmental Scan. October 2009. Accessible at: http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=14305. Accessed 2013 March 13.
4 Blaser DA, Lewtas AJ, et al. How to Define Specialty Pharmaceuticals – A Systematic Review. Am J Pharm Benefits. 2010; 2(6):371-380.
5 Schwartz RN, Eng KJ, et al. NCCN Task Force Report: Specialty Pharmacy. J Natl Compr Cnac Netw. 2010; 8[Suppl 4]:S1-S11. 6 Centers for Medicare & Medicaid Services. CMS.gov. Code Sets. Available at https://www.cms.gov/Regulations-and-Guidance/
HIPAA-Administrative-Simplification/TransactionCodeSetsStands/CodeSets.html. Accessed 2013 March 13. 7 ICD9Data.com. Free 2012 HCPCS Codes. Available at http://www.icd9data.com/HCPCS/2012/default.htm. Accessed 2013
March 13. 8 American Medical Association. CPT - Current Procedural Terminology. Available at http://www.ama-assn.org/ama/pub/physician-
resources/solutions-managing-your-practice/coding-billing-insurance/cpt.page? Accessed 2013 March 13. 9 Centers for Disease Control and Prevention - International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-
9-CM). Accessible at: http://www.cdc.gov/nchs/icd/icd9cm.htm. Accessed 2013 March 13. 10 Food and Drug Administration. Questions and Answers: Issuance of Three Draft Guidance Documents on Biosimilar Product
Development. Accessible at: http://www.fda.gov/Drugs/DevelopmentApprovalProcess/HowDrugsareDevelopednadApproved/ApprovalApplications/TherapeuticBiologicApplications/Biosimilars/ucm291186.htm. Accessed 2013 March 13.
As always, the AMCP dossier is a reactive deliverable provided in response to an unsolicited request.
What the AMCP Version 3.1 Format for Formulary Submissions Means to Drug Manufacturers
Karen Sandman, PhD, Senior Dossier Lead, Global Market Access Consulting
With the advent of the Version 3.1 Academy of Managed Care Pharmacy (AMCP) Format for Formulary Submissions, some may wonder how the changes will affect their submissions. The following is some guidance for dossiers in development, along with recently completed or established dossiers.
Dossiers in developmentAs of February 1, 2013, manufacturers are expected to use Version 3.1 for new AMCP dossiers. For those beginning development of a dossier, it will be straightforward to implement the new Format. If you have a dossier that is nearing completion, you may need to make adjustments if the product is a specialty pharma-ceutical, if it has a companion diagnostic test, or if the dossier cites comparative effectiveness research. These adjustments are not extensive, and they will help you to put forth a clearer body of evidence demonstrating the clinical and economic value of your product. For example, in a dossier implementing Version 3.1 for a specialty pharmaceutical indicated in an orphan condition, the increased page limits allow for an expanded discussion to educate the managed care audi-ence on the burden of illness and unmet need in a rare and not widely understood disease.
Recently completed dossiersIt can be frustrating to complete a months-long dossier development program, only to discover that the dossier may be now outdated due to the update. There are several options in this scenario. First, keep in mind that Version 3.1 only applies to new and updated dossiers released after February 1, 2013. If a dossier was released before February 1, 2013, and it complies with Version 3.0, then it is deemed to be not outdated. Nonetheless, you may find that implementing Version 3.1 would strengthen the dossier, particularly if your product is a specialty pharmaceutical, if it has a companion diagnostic, or if the dossier cites comparative effec-tiveness research. In such cases, it may be appro-priate to update the dossier right away, or perhaps you could plan a Version 3.1 update to coincide
with some expected new clinical trial data, new treatment guidelines, updated economic models, or information on a newly approved comparator. If your team is happy with the recently completed dossier, then a Version 3.1 update should be a relatively streamlined process that can be achieved in less than six weeks, depending on whether other updates are also planned.
Established dossiersFor most products, it is a good idea to update the AMCP dossier every few years. These updates allow you to include current label data for your product and comparators, the latest treatment guidelines, new clinical studies, longer-term follow-up of existing studies, economic models that reflect current costs and comparators, registry data, and the outputs of various health economic and outcomes research (HEOR) studies relating to your product. If it has been a few years since you last updated the dossier, you could consider combining a general update with implementation of Version 3.1.
A note on distribution of updated dossiersAs always, the AMCP dossier is a reactive deliv-erable provided in response to an unsolicited request. The AMCP Version 3.1 Format states: “During recent discussions, FDA regulatory staff has stated that individual unsolicited requests are required to obtain updates to product dossiers that include information on unlabeled uses. To avoid health systems having to submit potentially numerous unsolicited requests FDA staff agreed that health systems may include in their original unsolicited request for an evidence dossier, a statement requesting any new published and unpublished information on labeled and unla-beled uses, including updated dossiers for the specific product. NOTE: The request for updated information must pertain only to the specific information or dossier included in the original unsolicited request. Also, the request for updates must specify a length of time, e.g., 6 months.”1
HealtH economics, outcomes ReseaRcH, maRket access, Data analytics, epiDemiology
Should We Be Investing More in Mental Health Products? The Impact of Experience-based Utility Measures on Reimbursement Decisions
Kevin Marsh, PHD, Senior Research Scientist & European Director, Health Economics Modeling & Simulation, UBC; Chantelle Browne, MSc, Research Associate, Health Economics Modeling & Simulation, UBC, Jennifer Roberts, Dept. of Economics, The University of Sheffield
IntroductionRecent research into the impact of health on experience-based measures of utility suggests that a greater weight should be given to the impact of products on mental health outcomes than is currently the case. This article tests these claims and explores their implications for reim-bursement decisions.
Cost-effectiveness analysis plays an impor-tant role in decisions to reimburse products. For instance, the National Institute for Health and Clinical Excellence (NICE) requires that manufacturers submit evidence on the cost effectiveness of products. More precisely, they require that cost-effectiveness be estimated as the incremental cost per Quality Adjusted Life Year (QALY) gained.1 Whilst the QALY has been endorsed by a range of high profile organisa-tions, not just NICE, it is much criticised in the literature. Common critiques of the QALY include:
1. The instruments used to describe health states along standard dimensions, such as the EQ-5D2 and the SF-6D3, produce different results.4
2. Standardised health instruments are insuf-ficiently sensitive to the health impacts of specific diseases.5
3. Utility is not additive over time – the value of health states are not independent of time spent in them, or the order in which they are experienced.6,7
4. The QALY assumes that it is the objective of decision makers to maximise health, and ignores other objectives such as equity and fair-ness. This has been challenged based on ethical theory and public opinion.7,8
QALYs assign values to health states based on individual preferences, which have been obtained using approaches such as standard gamble, time trade off, and visual analogue
scale.4 NICE, for instance, requests general population preferences to be used, rather than those of patients.1 This approach requires that these preferences are rational and fully formed, something that has been famously challenged by behavioural economists.
The insights of behavioural economics - that people do not perform affective forecasts very well - means that QALYs may be subject to biases.9,10 That is, people do not behave like the rational agents of economic theory and do not always choose what is best for them. A key reason for this is that the process of eliciting preferences draws people’s atten-tion away from the possibility of adapting to a health state. When in a health state, people do not attend to being in that state all of the time – they “adapt” to the health state, and also direct focus at other aspects of their life. People’s preferences for avoiding health states fail, however, to anticipate this adaptation. As a result, preferences are a poor predictor of the experience of health.
Given these challenges, “experience utility” has been proposed as an alternative framework for estimating the value of health outcomes. Rather than asking people to anticipate what it would be like in a particular health state and give their preference for avoiding the health state, experience-based utility approaches elicit people’s evaluation of their well-being and uses statistical techniques to determine how their life experiences, including health, impact these assessments. A prominent example of this approach is the use of life satisfaction metrics. Survey respondents are asked to respond to the question “how dissatisfied or satisfied are you with your life overall”; answers to the question range from 1 “not satisfied” to 7 “completely satisfied.”
What would be the impact on healthcare of prioritising investments based on experience utility rather than preferences? A number of
What would be the impact on healthcare of prioritising investments based on experience utility rather than preferences?
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recent studies11,12 have estimated that such an approach would give a greater weighting to mental health outcomes than is the case with the preference-based approaches currently used to estimate QALY impacts.
The objective of this paper is threefold. First, to replicate recent work in this area11-13 using a more recent and larger data set. Second, to estimate the impact of experience utility-based tariffs on the utility decrements associated with different diseases. Third, to simulate the impact of such tariffs on the relative cost-effectiveness of technologies.
Step 1: The Impact of Health on Well BeingThe impact of health domains on well being was estimated using data for 61,699 respon-dents from the first wave of Understanding Society – a survey of 40,000 UK households.
Using the published algorithm,14 the SF-6D was generated from the SF-12. OLS (Ordinary Least Squares) regression models were then run to estimate the impact of SF-6D dimensions on life satisfaction, with life satisfaction rescaled from its original 1-7 scale to 0-1. Other factors known to influence life satisfaction were also included in the model, such as age, income, marital status and employment status.15
Figure 1 shows the tariffs for the different dimensions of the SF-6D derived using both the conventional standard gamble approach14 and life satisfaction scores. It demonstrates that, compared with the standard gamble, experience-based utility gives greater weight to the mental health dimension of the SF-6D, and less weight to the pain dimension. This is consistent with the findings of previous studies undertaken on different data sets.11,12
Step 2: Estimating Disease Utility DecrementsWhat would be the implications of adopting an experience-based approach, rather than a preference-based approach, for estimating the utility impact of specific diseases? Previous literature has, to the best of our knowledge, not answered this question directly.
Respondents to the Understanding Society survey report whether they had been told by a health professional that they suffered from one or more of 17 different health problems. Excluding those patients who reported more than one health problem, and focusing on just those diseases for which data was available for more than 1,000 respondents, health domain scores were combined with the tariffs derived from standard gamble and life satisfaction data to estimate the utility associated with health states.
Figure 2 shows the percentage change in utility decrement of each disease of adopting experience-based tariffs rather than the conventional standard gamble-based tariffs. It demonstrates that, on average, the utility losses would tend to increase when measured using experience-based utility. This is particularly the case for clinical depression and asthma. There is, however, more variation in the impact of experience-based approaches on the utility decrement of the most severe cases of diseases. The utility decrement associated with severe
Figure 1: Health Dimension Tariffs - Experienced Utility Places a Greater Value on Mental Health, Less on Pain
What would be the implications of adopting an experience-based approach, rather than a preference-based approach, for estimating the utility impact of specific diseases?
PF2
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arthritis, diabetes, and high blood pressure falls, while the utility decrement associated with severe clinical depression increases.
Step 3: Simulating the Impact on ICERsWhat impact would these alternative ways of measuring the utility impact of health states have on estimates of the cost-effectiveness of drugs? This was estimated for a hypothetical treatment that cost £10,000 and which would reduce the likelihood of the disease being expe-rienced over a 10-year period. The effect of the treatment – the probability that the intervention reduced the chance that people experience a disease – was defined as that which generated an ICER of £30,000 per QALY using standard
gamble-based tariffs. The change in the ICER was then estimated if the utility impact was calculated using experience-based tariffs.
Figure 3 illustrates how these experience-based estimates might impact estimates of the cost-effectiveness of treatments. It demonstrates that most treatments targeted to the average sufferer of the disease would have a lower ICER, with the exception of the treatment for arthritis. Of treatments targeted towards the more severe cases of the disease, however, only the treat-ment for clinical depression has a lower ICER, when valuing outcomes based on experience utility.
ConclusionExperience-based utility measures offer an alternative way to value health outcomes, overcoming some of the biases associated with conventional preference-based approaches. This study demonstrates that such measures, specifically life satisfaction, place more weight on mental health outcomes. As a consequence, health domain tariffs based on these metrics would impact the relative burden of illness and cost-effectiveness of treatments for different diseases. Treatments for average severity asthma, diabetes, depression and blood pressure would have lower ICERs. For treatments for severe versions of the diseases considered, only those for clinical depression would have lower ICERs. In turn, this would impact on the prices that different treatments can command.
Using experience-based utility to value health outcomes could then influence the reimburse-ment of treatments. It is important to note, however, that methods for estimating the experience utility impact of health are in their relative infancy. Further work is required to develop the methods further. For instance:
n The work reported in this paper should be replicated on future waves of Understanding Society to allow for individual effects on life satisfaction.
n Experience-based utility tariffs should be applied to estimates of the cost-effectiveness of actual treatments.
n Further work is required to understand why, for instance, health domains such as pain have a relatively small impact on life satisfaction.
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Figure 2: The Impact on Utility Loss When Measured Using Life Satisfaction Rather Than Standard Gamble
Figure 3: Change in Incremental Cost-effectiveness Ratio if Based on Experience Utility
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n Work is required to understand how to anchor tariffs derived from life satisfaction data.
n Work is required to understand how time spent in a health state influences its impact on life satisfaction.
n Work is required to understand how best to communicate the result of life satisfaction research to decision makers and the public.
References 1 National Institute for Health and Clinical Excellence (NHS). Guide to the Methods of Technology Appraisal. 2008 June. Available
at http://www.nice.org.uk/media/b52/a7/tamethodsguideupdatedjune2008.pdf. Accessed 2013 February 15. 2 Dolan P. Modeling Valuations for EuroQol Health States. Med Care. 1997 Nov; 35(11):1095-1108. 3 Brazier J, Roberts J, Deverill M. The Estimation of a Preference-based Measure of Health from the SF-36. J Health Econ. 2002
Mar; 21(2):271-292. 4 Revicki DA, Lenderking WR. Methods and Issues Associated with the Use of Quality-adjusted Life-years. Expert Rev Pharmaco-
econ Outcomes Res. 2012 Feb; 12(1):105-114. 5 Wailoo A, Davis S, Tosh J. The Incorporation of Health Benefits in Cost Utility Analysis Using the EQ-5D. Report by the Decision
Support Unit. 2010 Nov 15. Available at http://www.nicedsu.org.uk/PDFs%20of%20reports/DSU%20EQ5D%20final%20report%20-%20submitted.pdf. Accessed 2013 February 15.
6 Smith MD, Drummond M, Brixner D. Moving the QALY Forward: Rationale for Change. Value Health. 2009 Mar; 12 Suppl 1:S1-4.
7 Weinstein MC, Torrance G, McGuire A. QALYs: The Basics. Value Health. 2009 Mar; 12 Suppl 1:S5-9. 8 Nord E, Daniels N, Kamlet M. QALYs: Some Challenges. Value Health. 2009 Mar; 12 Suppl 1:S10-5. 9 Kahneman D. Determinants of Health Economic Decisions in Actual Practice: The Role of Behavioural Economics. Presented at:
ISPOR 10th Annual International Meeting; May 15-18, 2005; Washington, DC, USA.10 Dolan P, Kahneman D. Interpretations of Utility and Their Implications for the Valuation of Health. The Economic Journal. 2008
Jan; 118(525):215-234.11 Dolan P, Lee H, Peasgood T. Losing Sight of the Wood for the Trees: Some Issues in Describing and Valuing Health, and Another
Possible Approach. Pharmacoeconomics. 2012 Nov 1; 30(11):1035-1049.12 Mukuria C, Brazier J. Valuing the EQ-5D and the SF-6D Health States Using Subjective Well-being: A Secondary Analysis of
Patient Data. Soc Sci Med. 2013 Jan; 77:97-105. [Epub 2012 Nov 19]13 Dolan P. Using Happiness to Value Health. London: The Office of Health Economics. 2011 Nov. Available at http://www.ohe.org/
publications/article/using-happiness-to-value-health-98.cfm. Accessed 2013 February 14.14 Brazier JE, Roberts J. The Estimation of a Preference-based Measure of Health from the SF-12. Med Care. 2004 Sep; 42(9):851-
859.15 Dolan P, Peasgood T, White M. Do We Really Know What Makes Us Happy? A Review of the Economic Literature on the Factors
Associated with Subjective Well-being. J Econ Psychol. 2008; 29(1):94-122.
ACKNOWLEDGMENTSData Source: University of Essex. Institute for Social and Economic Research and National Centre for Social Research, Understanding Society: UK Data Archive September 2011. SN: 6614. Understanding Society is an initiative by the Economic and Social Research Council, with scientific leadership by the Institute for Social and Economic Research, University of Essex, and survey delivery by the National Centre for Social Research.
Over the course of the last few years, UBC has been a key player in supporting our clients in attaining successful reimbursement deci-sions, and even in overturning initially negative decisions, in a wide array of therapeutic areas including cardiovascular disease, dementia, and rheumatoid arthritis. Instrumental to this success has been the development and, just as impor-tantly, presentation of rigorous, evidence-based modeling studies. There is no question that models, especially in many European markets, Canada and Australia, do drive decision making. It is also true, that incremental cost-effectiveness
ratios (ICER) outcomes are a metric that decision makers are using. A common misunderstanding, however, is the belief that the ICER, in and of itself, is driving decision making. A recent review of an SGLT2 inhibitor by NICE provides ample evidence that submitting a model with a favor-able ICER is not enough. Submitting a poorly constructed and poorly described model which has not undergone detailed validation and verification is not just unhelpful, it is especially harmful and a sure path to a negative decision. The submission of low quality model-based evaluations increases the perception of uncer-
Models Do Drive Decision Making – But Do They Take You Where You Want to Go?
Denis Getsios, Senior Research Scientist and Senior Director, Health Economics Modeling & Simulation
Submitting a poorly constructed and poorly described model which has not undergone detailed validation and verification is not just unhelpful, it is especially harmful and a sure path to a negative decision.
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tainty, fosters distrust, and puts the submission into jeopardy as a whole.
While guaranteeing a positive reimbursement decision via modeling is a false promise, there are elements that go a long way to improving chances of success - and especially avoiding unnecessary harm - and forming the philosophy of our model-based studies. These start with a deep understanding of the underlying condition and the value of the product being assessed, as well as the existing evidence base and previous health technology assessments (HTA). This then forms the basis of the development of rigorous, transparent, flexible, conservative and credible models that must undergo extensive verification and validation.
This sounds simple enough, but there are a number of possible hazards and common missteps. A few of the most prevalent and harmful in our experience are the following:
n Starting with the premise that the main objective of the model is to produce a “good ICER”. While certainly desirable, that mindset leads to flawed-decision making and can lead to biases that become readily apparent to any competent reviewer.
n Deciding on the model approach at the outset. We see countless requests for proposals where the modeling technique is already pre-specified, often on the basis of historical prec-edent. It is a continual challenge to effectively communicate why our proposals do not provide details of a certain design or approach. By deciding at the beginning how to answer the question, the potential for underestimating the benefits of a new intervention are enormous. At times, tried and true may be good enough and one should not underplay the importance of previous research, but research evolves, methods improve, and decision questions and the value of different products do vary.
n Cherry-picking inputs. It is always tempting to select inputs that lead to the most favorable ICERs. However, if you are cognizant of the reasoning for this approach, then it is almost a guarantee that reviewers will be as well, and here you risk harm, in that the reviewer is then free to select their own input rather than those based on the best available evidence. No model, no matter how sophisticated, can hide bad data.
n Model rigidity. One important consideration when developing models is flexibility. A design that does not allow for easy modification to test various scenarios is extremely risky. Even with a clear decision question and clear ratio-nale for a model structure, the model must be able to be modified to answer potential ques-tions or challenges. In deciding on the best model to use, questions and scenarios should be anticipated, and then a model should be developed that can be quickly modified, or better yet, handle structural sensitivity analyses. This can avoid last-second half-measures when responding to queries and reduce any percep-tion of uncertainty in predicted outcomes.
n The “kitchen sink” approach. Another common temptation in pursuit of a “good” ICER is to include any possible benefit for an intervention, no matter how speculative or weak the data are, and regardless of how trivial the impact on predictions. Unless there is a solid evidence base, or the influence on predictions is great, inclusion of such benefits will again tend to increase the perception of both bias and uncertainty.
The principles of successful model-based economic evaluations may seem straightfor-ward, but implementation takes experience and discipline. Everything first rests on understanding the decision problem. This means having an in-depth knowledge of the medical condition, its impact on patients, possible treatment path-ways and the presumed benefits and risks of interventions, both novel and existing. Some of the important factors determining likelihood of success include:
n Consulting and consulting early. While any decent modeler is much more than just a programmer, the opinions of therapeutic experts are often critical in framing the deci-sion problems and understanding what needs to be done to address these. Consulting early also means starting early. Developing defen-sible models, and especially collecting and synthesizing the evidence needed to populate these models, takes time.
n Select the best modeling approach. This might seem an obvious statement, but the temptation to rely on historical precedent is strong. This absolutely does not mean dismissing previous research, but deciding
The principles of successful model-based economic evaluations may seem straightforward, but implementation takes experience and discipline.
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to adopt the approach of a previous model should not be made before a thorough evalu-ation of the project objectives to determine the optimal approach, as well as an understanding of the costs (if any) of using previous work. The reversal of NICE’s decision on reimbursement of cholinesterase inhibitors for Alzheimer’s disease serves as an excellent case study of how historical modeling approaches failed to fully capture the value of these therapies, necessi-tating new thinking and novel approaches. On the other hand, recent decisions on novel oral anti-coagulants such as apixaban demonstrate how more traditional approaches may be more than adequate. Reviewing previous models and previous HTA reviews of these models is vital, as each case is unique.
n Be conservative. There is virtually no point in doing otherwise. In markets where cost-effectiveness analyses are an important determinant of decision making, reviewers are not naïve. A non-conservative approach to modeling and selection of model inputs leads to distrust in the submission and will almost certainly be challenged. For example, an assumption made about a 30% reduction in health utilities for your comparator’s adverse event based on consulting Dr. X will most likely be changed to 0% even if the truth lies somewhere in between.
n Be transparent. Beyond documenting data sources, be sure to make explicit all assump-tions and limitations. Having them “discovered” does not help your case. Electronic models, when submitted, should also be clear and easy to navigate and understand. For more complex models, user guides and other forms of supple-mentary documentation can be crucial.
n Validation and verification. Test everything and test it again. This obviously applies to the technical accuracy of the model, but predic-tions should also be validated when possible against observed data. The recent ISPOR-SMDM recommendations on validation and verification of models1 provide an excellent, if somewhat daunting guide to what, unfortu-nately, is often treated as an afterthought.
The decision in choosing the right model for the right need is one that needs to be taken seriously, as models do drive decision making. Establishing scientific rigor in the selection process and adhering to basic principles can lead to successful model-based economic evalua-tions, and ultimately, improving the chances of overall product success with positive reimburse-ment decisions. Make sure your model takes you where you want to go.
Reference 1 Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB. ISPOR−SMDM Modeling Good Research Practices Task
Force. Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force -7. Value Health. 2012 Sep-Oct; 15(6):843-850.
The RE-ADAPT Study: Will it Prove to be a Proof of Concept?
Bryan R. Luce, PhD, MBA, Senior Vice President for Science Policy and K. Jack Ishak, PhD, Senior Research Scientist & Senior Director, Biostatistics
In this article, we report on an extremely interesting and, we think, important study that may prove to be a sufficient “proof of concept” to stimulate private and public invest-ment in future Bayesian adaptively designed comparative effectiveness research (CER) trials. The study, RE-ADAPT (REsearch in ADAptive methods for Pragmatic Trials) is an outgrowth of the PACE Initiative (recall that PACE stands for Pragmatic Approaches to Comparative Effectiveness).
BackgroundThe PACE Initiative was launched in 2008 with the belief that “Without major changes in how we conceive, design, conduct and analyze RCTs, the nation risks spending large sums of money inefficiently to answer the wrong questions — or the right questions too late.”1 Early on, the PACE Initiative focused on Bayesian adaptive methods as a possible way to achieve such a transformational change. At its first meeting, the PACE Advisory Board recommended that a
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“proof of concept” would be needed before we could expect investment in a real life Bayesian adaptive CER trial since there had not previously (nor has yet) been one.
In response to a 2009 National Institutes of Health (NIH) solicitation for CER methods research, several of us collaborated with the University of Maryland Schools of Pharmacy and Medicine (Dr. Daniel Mullins as Primary Investi-gator), Berry Consultants, and Dr. Dave Vanness (University of Wisconsin) and were awarded through the University the three year, $1.5 million grant “Do Bayesian Adaptive Trials Offer Advantages for CER?”, now dubbed RE-ADAPT. We are in the middle of year three.
The RE-ADAPT Study simulates a re-execution of the NIH/Department of Veteran Affairs-funded Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT)2 using the actual ALLHAT data to evaluate whether a Bayesian adaptive design might have accomplished the original ALLHAT objectives more efficiently. We selected ALLHAT as the case study because it was large (42,418 patients); nationally prominent; compared three active antihypertensive medications (alpha blocker, calcium channel blocker, alpha blocker) to a diuretic within community care settings; a public-use, patient-level dataset was avail-able; and the trial was costly ($135 million) and sufficiently lengthy (eight years) that practice patterns and, thus, possibly clinical questions had actually changed significantly during the course of the trial (e.g., combination versus monotherapy became more standard). Enrolled patients had at least one cardiovascular disease risk factor besides hypertension. The primary outcome was reduction in fatal coronary heart disease (CHD) and nonfatal myocardial infarction (MI). Interestingly, the original ALLHAT design did incorporate conditions for arm dropping when an arm is performing poorly, early stopping when an arm is identified as superior to control, and for early study termination.3
The simulation exercise consists of five components:
1. A systematic literature review and deriva-tion of priors based on literature existing when ALLHAT was designed. This provided the data upon which prior evidence distributions could be derived.
2. Identification of possible adaptations that may improve trial efficiency, which involved specifying possible adaptive features (e.g., adap-tive randomization or arm dropping) that might be included in such a trial.
3. Construction of alternative Bayesian adap-tive designs for ALLHAT by selecting combina-tions of specific adaptive features from Step 2. This included pre-specifying designs, including timing and frequency of interim analyses when adaptations may occur, and establishing a series of thresholds (e.g., early stopping bounds) when adaptation may occur.
4. Selection of one optimal Bayesian design for implementation by selecting from those developed in Step 3. (Note: The two primary designers of the simulation process remained blinded to ALLHAT data. Both were previously unaware of the ALLHAT trial results, did not read the clinical outcome papers during the design process and relied only on the original ALLHAT protocol and discussions with both cardiovas-cular experts and the ALLHAT primary designer and statistician.)
5. Execution of the Bayesian adaptive designs using actual ALLHAT data to assess performance of both the chosen optimal design and all others considered, comparing all with original ALLHAT design. (Note: Whereas, in reality, a single design must be chosen for a trial, since this is a simu-lation exercise, we demonstrate features and suitability of all designs considered.)
It’s interesting to note that whereas execution in Step 5 was a simulation of the ALLHAT trial, Steps 1-4 are virtually identical to the steps that would have and must take place in a real world design of a Bayesian adaptive trial.
Identifying possible adaptations that may improve trial efficiencyThree types of adaptations were considered in our simulation exercise: 1) adaptive randomiza-tion, 2) adaptive arm dropping, and 3) early stopping. Both adaptive randomization and arm dropping may occur during the accrual stage. Adaptive randomization and arm dropping serve two key purposes: they increase the probability that patients randomized later in the trial receive a beneficial therapy; and they can increase statis-tical power by prioritizing data gathering for treatments where the research question remains
Adaptive randomization and arm dropping serve two key purposes: they increase the probability that patients randomized later in the trial receive a beneficial therapy; and they can increase statistical power by prioritizing data gathering for treatments where the research question remains more uncertain.
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more uncertain. Furthermore, by performing multiple prospectively defined interim analyses during accrual and follow-up phases, the trial can stop early if primary goals are met or it becomes evident that additional information is unlikely to lead to a significant conclusion.
Selection of Optimal Design and Implementation Seven potential designs were constructed, all of which allowed early stopping for futility or success, and all incorporated different adapta-tion rules for randomization and arm dropping.
Next, we evaluated the operating characteristics of each of the seven candidate adaptive designs and compared one another by simulating trials across a variety of possible effectiveness scenarios. This involved simulating 1,000 trials with each design to understand the potential performance measured in terms of expected (mean) sample size and duration of the trial, power, probability of stopping early for success or futility, and proportion of patients random-ized to the best therapy for five different efficacy scenarios:
1. Null: no comparators arms better than control
2. Alternative: all equally better than control
3. One Works: one better than control and the other two equal to control
4. Better & Best: one best, one slightly better, and one equal to control
5. Worse: all are equally worse than control
The adaptive design scheme that performed optimally was one with probability-weighted adaptive randomization and probability-based adaptive arm dropping. Thus, our primary anal-ysis compares this selected design to the ALLHAT trial in terms of trial characteristics and efficiency, patient assignment and clinical outcomes. Also, if this were a real situation, our selected adap-tive design scheme would have been the design scheme of the Bayesian adaptive ALLHAT itself. However, given that our study is a simulation, after all, we also ran the simulated comparisons of each of the other six “non-selected” design schemes to evaluate how each respective design option might have performed.
Discussion In this article, we have not done justice (by a long shot) in terms of describing the detailed and extensive methodological protocol we followed in designing and executing RE-ADAPT. However, at the time of this writing, a manu-script describing in detail our research protocol is in review. We are guardedly optimistic that it will be available to interested reviewers soon. Further, we now have generated our simulated findings/comparisons … and they are rather interesting, but are embargoed at this point in time. However, for those attending the ISPOR Annual Meeting in New Orleans (May 18-22), key study investigators will be presenting both the methodology and our findings in the following sessions:
n Issue Panel (ID# IP8) Can Bayesian Adap-tive CER Trial Designs Bridge The Needs Of Patients, Payers, Regulators And Manufac-turers? ISSUE PANELS - SESSION II. Tuesday, May 21, 2013, 11:00 AM - 12:00 PM. Dr. Jack Ishak (UBC) will moderate; Dr. Jason Connor (Berry Consultants) will present the study design and findings; panelists include: Rachael Fleurence (PCORI), Charles Barr (Genentech) and Robert T. O’Neill (FDA) [Invited].
n Podium Presentation (ID# CE3) Advantages Of Bayesian Adaptive Trials For Comparative Effectiveness Research (CER): “RE-ADAPT”ING ALLHAT. RESEARCH PODIUM PRESENTA-TIONS - SESSION II, Comparative Effectiveness Research Studies, Monday, May 20, 2013, 3:45 PM - 4:45 PM. Dr. Kristine Broglio, a Bayesian biostatistician of Berry Consultants will be presenting.
Final CommentsThe RE-ADAPT study is an ambitious effort to produce – via a full-blown, patient-level simula-tion – valid “proof of concept” concerning the feasibility and usefulness of Bayesian adaptive methods for CER trials with the ultimate aim of stimulating future public and/or private invest-ment in them. We look forward to comments and constructive criticism of our approach.
Acknowledgements: Most importantly, we wish to acknowledge the generous grant support from the NIH/NHLBI (1RC4HL106363-01). We also appreciate the contributions of the following individuals to the RE-ADAPT study:
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Daniel Mullins (RE-ADAPT PI), Don Berry, Kristine Broglio, Jaime Caro, Jason Connor, Barry Davis, Rachael Fleurence, Stephen Gottlieb, Wallace Johnson, Jörgen Möller, Ebenezer Oloyede, Eberechukwu Onukwugha, Elijah Saunders, Fadia Shaya. Finally, we wish to express our appreciation to the many PACE Initiative advi-sors and supporters without whom this project
would never have been conceived nor initiated. Dr. Luce is the co-PI and Dr. Ishak is a senior research biostatistician and modeler for the RE-ADAPT study.
Comparative Effectiveness Research: The Need for Transformational Change. Ann Intern Med. 2009 Aug 4; 151(3):206-209. [Epub 2009 Jun 30]
2 Davis BR, Cutler JA, Gordon DJ, Furberg CD, Wright JT, Jr., Cushman WC, et al. Rationale and Design for the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT Research Group. Am J Hypertens [Clinical Trial, Multicenter Study, Randomized Controlled Trial, Research Support, U.S. Gov’t, P.H.S.] 1996 Apr; 9(4 Pt 1):342-60.
3 Davis BR, Cutler JA. Case 18: Data Monitoring in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial: Early Termination of the Doxazosin Treatment Arm. In: DeMets DL, Furberg CD, Friedman LM, editors. Data Monitoring in Clinical Trials:A Case Studies Approach. New York, NY: Springer, 2006, p. 248-259.
Incorporating the Payer Perspective into Clinical Development Programs
Carol Ware, CPC, Senior Research Manager and Beth Hahn, PhD, Managing Director, Global Market Access Consulting
Manufacturers developing a new therapy are focused on the Food and Drug Administration (FDA) requirements for the design of clinical trials to meet regulatory approval. The input of Key Opinion Leaders (KOLs) and prescribers are integral parts of the process and design of these programs. Payers are less frequently included as part of this discussion, yet payers often are essential to market uptake as they have the major financial stake and determine access for a new therapy post-approval. Unlike KOLs and prescribers, payer input will not benefit all clinical development programs to the same extent. Those that will benefit most will be based on four important factors to consider: 1) the size of the population, 2) the cost of treating the disease, 3) the setting of care, and 4) the number of competitors.
The Size of the Population Diseases that affect large populations are ideal for gaining the payer perspective early. Payers are intimately familiar with the interventions that are used to treat the largest numbers in their populations and can readily identify their top spend by category or disease state. Their “top 20 lists” in population size are usually also that of top expenditures. As such, payers carefully review and monitor these areas, which allows them to assess whether an attribute considered
clinically important would make a difference for their own population’s health and possibly result in a decrease in spend.
The Cost of Treating the Disease StateAside from number of the population affected, payers are concerned with their spend for thera-pies by disease. Orphan indications notwith-standing, there are disease states for which costs are high and are an area of focus despite the relatively modest population size. An example is multiple sclerosis (MS), with a population of approximately 400,000 in the U.S.1 Payers consider the therapies available to treat MS as a high expense that is in their top categories of spend and are looking for ways to differen-tiate and determine value for current and new therapies.2
The Setting of Care The setting of care in which a drug will be administered is also an important consider-ation for payer input. Inpatient hospital care is the most expensive setting of care, yet it is the setting where payers can exert the least amount of control over how the therapy is used. Likewise, payers use prospective payment rates to reimburse inpatient therapies, leaving therapy choice up to the facilities and providers. Payers will not focus their limited time on therapies
All of these factors – population size, cost, setting of care, competitive choices – translate to utilization controls payers can implement in the form of prior authorization, step therapy, generic failure, etc. (i.e., factors that allow payers to establish criteria for access).
Is this factor considered in whether a drug will beincluded on formulary?
How relevant is it?
How important is it?
Would these data makea difference in how you
manage the drug onformulary?
What is the impact of factor "X" on how drugs
in this category are currently managed?
Concretely and specifically,would this cause you to move this therapy ahead
of other therapies?
they cannot manage; therefore, they have little inclination to provide clinical design direction for an inpatient therapy.
The Number of CompetitorsPayers have more leverage and greater flexibility in applying utilization controls in areas where there are many drugs to choose from, especially if there is little differentiation between them. In contrast, areas for which there are few therapies (e.g., ultra orphan diseases) are not as suitable for soliciting payer input. The key for manufac-turers introducing a new drug into a crowded market is to ascertain the clinical improvement required by the payers to separate the new entrant from the existing therapy. Though this may elicit a response such as “20% improve-ment” or some other quantifiable improvement over baseline that mirrors feedback from KOLs, payers’ interest in relative improvement reflects their attention to cost savings and the data needed to demonstrate those savings.
All of these factors – population size, cost, setting of care, competitive choices – translate to utilization controls payers can implement in the form of prior authorization, step therapy, generic failure, etc. (i.e., factors that allow payers to establish criteria for access).
The So What for PayersPayers can and will respond to the same ques-tions asked of clinicians when asked to rate the usefulness of patient-rated instruments or clinical measures; yet, this exercise misses the
mark in understanding the power of payer input. Central to seeking payer input is a focus on the action that payers will take based on the data. Specifically, does that data have a role in determining formulary inclusion, tier place-ment (i.e., preferred versus non-preferred), access controls (e.g., prior authorization or step therapy), or developing medical policy criteria (inclusion/exclusion characteristics) for drugs covered under the medical benefit?
Areas that matter most to payers and have a role in management include efficacy, safety, and cost, but with caveats:
Efficacyn Payers can clearly define the efficacy attributes
that drive coverage decisions
n Efficacy versus a comparator is more valuable than efficacy versus placebo, but payers will make their own drug-to-drug comparisons if no active comparator is used in trials
n Payers will focus on the increase over placebo they expect to see compared with current therapies
Safety/Tolerabilityn Claims of safety equivalent to placebo are not
credible as payers question that any drug can reach this level of safety
n Black Box warnings do not lower a product’s value as it is no longer a rarity for drugs to carry such warnings
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n Risk Evaluation and Mitigation Strategies (REMS) do not sway coverage decisions as payers view REMS as a provider/FDA/manufac-turer concern
n Supported claims of superior tolerability claims are valued as tolerability relates to adherence to the prescribed treatment
n Safety/tolerability over time as long-term data matters to payers as they want to see what happens in the market and outside of the confines of clinical trials
Cost n Expectation of lower medical resource use,
supported by data, gets payers’ attention
n Payers accept cost models if they are able to use them with their own data
n While total medical cost is compelling, more important to most payers is the cost of the drug/therapy alone
A Note About CostDespite the importance of cost/price for market access, the focus on seeking input for clinical
trials needs to be as devoid of cost as possible. Removing cost from the equation and testing product attributes solely on clinical value allows a manufacturer to determine the relative improve-ment needed to influence payers in decision making and support market access.
ConclusionPayer input should be part of the clinical trial/evidence generation plan development, yet it is important to incorporate this type of research in situations where it would be most useful and valuable. Consider the size of the expected patient population, the costs of treatment, including the setting of care, and the competi-tive market basket – as well as the areas that matter the most to payers. Payers are genuinely concerned about and engaged in efforts to improve the health of their covered populations in an efficient and cost-effective manner and do have much to add to the process for the right programs.
References 1 Themcfox.com website. Multiple Sclerosis Facts. Accessible at http://www.themcfox.com/multiple-sclerosis/ms-facts/multiple-
sclerosis-symptom-facts.htm. Accessed 2013 March 19. 2 UBC data on file
Rasch Model Analysis and Small Sample Size
Dennis A. Revicki, PhD, Senior Vice President; Wen-Hung Chen, PhD, Research Scientist; Heather Gelhorn, PhD, Research Scientist, Outcomes Research
The traditional iterative cycle of qualitative and quantitative methods for developing and evaluating the measurement properties of new PRO measures is consistent with the U.S. Food and Drug Administration (FDA) guidance on PRO endpoints for product labeling.1 Recently, it has been suggested that data from small sample cognitive interviewing studies might be used to examine the preliminary psychometric char-acteristics of new PRO measures, in particular, through the use of Rasch analysis.2 However, there has been recent debate regarding the value and potential for increasing the efficiency of the instrument development process through quantitative analysis of data from relatively small samples.3 Although the suggestion is intriguing,
we thought the approach should be tested empirically before widespread adoption of Rasch analysis early in the process of instrument development.
We completed a simulation analysis of existing well-characterized data to evaluate the use of Rasch analysis in various sample sizes ranging from 30 to 250 and compared the results with an analysis of the item data from the original sample (N=800). Data from the PROMIS pain behavior item bank4 were used to randomly generate subsets of small samples for the Rasch modeling. Exploratory and confirmatory factor analysis was used to identify a 10-item unidimensional short form scale for use in
There has been recent debate regarding the value and potential for increasing the efficiency of the instrument development process through quantitative analysis of data from relatively small samples.
this simulation study. The advantage of using items from a known item bank is that any issues detected with item performance can be attributed mostly to the sample size, since there is already considerable evidence on the characteristic and functioning of each of the items. Samples of 30, 50, 100, and 250 were randomly drawn 10 times each from the total sample of 800 subjects. In addition, one set of 10 targeted samples, with a sample size of 30, was also generated. In these targeted samples, 30 cases were selected based on their 11-level (0–10) pain intensity numeric rating scale scores (NRS) classified into four categories — no pain (0), mild pain (1–3), moderate pain (4–6), and severe pain (7–10)5 — with a ratio of 3:9:9:9, respectively. The ratio was selected so that there were equal numbers of observations in each of the three pain severity categories that reported some pain, and that the no pain category had the fewest observations. Rasch analysis was conducted for each of the random samples (both targeted and non-targeted), as well as the full sample. RUMM2030 was used for the Rasch analysis.6 Based on the Rasch modeling results,
the average percentage of extreme scores (i.e., lowest response categories or highest response categories) ranged from 4% for the targeted sample to 20% for a non-targeted sample of size 30. Extreme scores are non-informative in terms of fitting Rasch model; hence, as default, the cases with extreme scores are excluded from the analysis by RUMM2030. The impact of extreme scores on the small sample size data sets (N <– 50) was greater than in those with larger sample sizes (N >– 100).
In Rasch modeling, estimation of threshold parameters is problematic when there are no observations for the corresponding response categories, known as a null response category. In general, null categories should be avoided in models as there is no data for the estimation of the corresponding threshold parameters. The average percentage of null categories ranged from 0% in the total sample to 22% in the 30-case sample. The largest number of null categories was observed in the smaller samples (15%–22%), including the targeted sample (14%) compared with the larger samples (0%–5%).
Table 1. Average number of items with incorrectly ordered response categories
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Figure 2. Item map for one of the non-targeted data sets with a sample size of 30
Figure 1. Item map of the full sample size (n=800)
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Figure 2. Item map for one of the non-targeted data sets with a sample size of 30
Figure 2. Item map for one of the non-targeted data sets with a sample size of 30
One of the main reasons for using Rasch modeling to evaluate item performance is the ability to examine whether response catego-ries are ordered correctly. When the response categories exhibit incorrect ordering, it is an indication that the response categories are not appropriate and that they should be modified or that the item should be removed. Sample size is an important factor since robust estima-tion of the threshold parameters depends on having sufficient observations for each of the corresponding response categories. Table 1 shows the average number of items with incorrect ordering identified from the Rasch modeling for the five simulation sets and the full sample. More items were identified as incor-rectly ordered when sample sizes were small, whereas the results from the full sample indi-cated that no item was incorrectly ordered. For the targeted sample, there was still an average of 37% of the items that were identified as incorrectly ordered.
In the application of Rasch modeling for the purposes of item evaluation, the person separa-tion index (PSI) is commonly examined. The PSI is a reliability measure similar to Cronbach’s alpha coefficient. The simulation study results show that PSI is not influenced by sample size (0.91–0.93), though the PSI increased slightly when the sample was targeted (0.93) to cover the full range of the underlying construct.
Rasch modeling is a useful method in that it
can be used to generate a linear representa-tion of the items comprising the instrument, and at the same time, allows person attributes to be placed on the same measurement scale. This characteristic of the Rasch model allows the examination of the alignment between the items and the distribution of the target popula-tion. Selected item maps from the full sample and for case simulation sets of size 30 are shown in Figures 1 to 3. These figures each tell a slightly different story about the same 10 items. The best alignment was seen for the full sample (Figure 1) and for the larger samples (results not shown) with scores ranging between 5 and -6 in the full sample. The random sample of 30 showed the worst results, that is, no item was located between the score range of 0 to 4 and no person had an underlying score greater than 0. For this non-targeted random sample of size 30, the item map suggested that the align-ment was not good. The alignment between the subjects and the item thresholds was better for the targeted sample (n=30), although the results of the targeted sample were similar to the random sample of 50.
Based on the simulation study, small sample sizes had a significantly negative impact on Rasch modeling-based parameter estimates, and instrument developers are cautioned against applying Rasch analyses in sample sizes of less than 100 participants. The Rasch analysis of the targeted sample of 30 subjects was comparable
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APRIL 2013 EvidEncE mattErs
to results obtained from the random sample of 50 subjects, although this information was still insufficiently informative and too inaccurate to form the basis for critical decisions regarding item modification or deletion. The simulations demonstrated that there were more stable estimates observed in samples that were of size 100 or greater. Based on the results of the study Rasch modeling on small sample sizes is not recommended. Although a small sample that is targeted to cover the full range of the underlying construct slightly improves some of the parameter estimation, it does not completely alleviate the disadvantages of conducting Rasch analyses on sample sizes of less than 50 participants. Caution should be exercised in interpreting the findings of Rasch modeling on small sample sizes, and instrument developers
should not make revisions to instruments under development based on these small sample Rasch analysis results.
The results of Rasch analyses based on small samples have the potential to be misleading. Rather than providing greater efficiency in instru-ment development, this approach may result in erroneous item-level decisions and consequently contribute to the development of less robust instruments. Truly informative models and more robust item parameter estimates are more likely to be generated in larger sample sizes and the small sample Rasch methods are, therefore, not recommended.
References 1 Food and Drug Administration. Guidance for Industry on Patient-reported Outcome Measures: Use in Medical Product Develop-
ment to Support Labeling Claims. Federal Register. 2009; 74(235): 65132–65133. 2 Hudgens S, Globe D, Burgess SM. Utilization of Rasch Measurement Models for Assessing Validity: A Mixed Methods Approach.
Workshop presented at International Society Pharmacoeconomics and Outcome Research17th Annual International Meeting, Washington, DC, 2012.
3 Hudgens S, Norquist J, Wyrwich KW, Coons SJ, Lenderking WR. Perspectives on Mixed Methods to Assess Content Validity of a PRO Measure. Presented at the Industry Advisory Committee Symposium, International Society for Quality of Life Research 19th Annual Conference, Budapest, 2012.
4 Revicki DA, Chen WH, Harnam N, Cook KF, Amtmann D, Callahan LF, Jensen MP, Keefe FJ. Development and Psychometric Analysis of PROMIS Pain Behavior Item Bank. Pain. 2009; 146:158–169.
5 Serlin RC, Mendoza TR, Nakamura Y, Edwards KR, Cleeland CS. When is Cancer Pain Mild, Moderate or Severe? Grading Pain Severity by Its Interference with Function. Pain. 1995; 61(2): 277–284.
Figure 3. Item map for one of the targeted data sets with a sample size of 30
Based on the simulation study, small sample sizes had a significantly negative impact on Rasch modeling-based parameter estimates, and instrument developers are cautioned against applying Rasch analyses in sample sizes of less than 100 participants. The Rasch analysis of the targeted sample of 30 subjects was comparable to results obtained from the random sample of 50 subjects, although this information was still insufficiently informative and too inaccurate to form the basis for critical decisions regarding item modification or deletion. The simulations demonstrated that there were more stable estimates observed in samples that were of size 100 or greater. Based on the results of the study Rasch modeling on small sample sizes is not recommended. Although a small sample that is targeted to cover the full range of the underlying construct slightly improves some of the parameter estimation, it does not completely alleviate the disadvantages of conducting Rasch analyses on sample sizes of less than 50 participants. Caution should be exercised in interpreting the findings of Rasch modeling on small sample sizes, and instrument developers should not make revisions to instruments under development based on these small sample Rasch analysis results. The results of Rasch analyses based on small samples have the potential to be misleading. Rather than providing greater efficiency in instrument development, this approach may result in erroneous item-level decisions and consequently contribute to the development of less robust instruments. Truly informative models and more robust item parameter estimates are more likely to be generated in larger sample sizes and the small sample Rasch methods are, therefore, not recommended. For more information, please contact [email protected], [email protected], or [email protected].
Figure 3. Item map for one of the targeted data sets with a sample size of 30
SHORT COURSESSaturday, May 18, 8:00 a.m. - 5:00 p.m.Bayesian Analysis - Overview and ApplicationsFaculty: Christopher Hollenbeak, PhD, Assoc. Prof., Surgery and Public Health Sciences, Penn State College of Medicine; David Vanness, PhD, Asst. Prof., Univ. of Wisconsin, and Visiting Scientist, Health Economics and Science Policy, UBC
Sunday, May 19, 8:00 a.m. - 12 p.m.Discrete Event Simulation for Economic Analyses - ConceptsFaculty: J. Jaime Caro, MDCM, FRCPC, FACP, Adjunct Prof. of Medicine, Adjunct Prof. Epidemiology & Biostatistics, McGill Univ., Canada, and Sr. VP Health Economics, UBC; Jörgen Möller, MSc Mech Eng, VP Modeling, UBC, Assoc. Researcher, Div. of Health Economics, Lund Univ., Sweden; Denis Getsios, Sr. Dir., Sr. Research Scientist, UBC
Sunday, May 19, 1:00 p.m. - 5:00 p.m.Discrete Event Simulation for Economic Analyses - ApplicationsFaculty: J. Jaime Caro, MDCM, FRCPC, FACP, Adjunct Prof. of Medicine, Adjunct Prof. Epidemiology & Biostatistics, McGill Univ., Canada, and Sr. VP Health Economics, UBC; Jörgen Möller, MSc Mech Eng, VP Modeling, UBC, Assoc. Researcher, Div. of Health Economics, Lund Univ., Sweden; Denis Getsios, Sr. Dir., Sr. Research Scientist, UBC
PLENARY SESSIONWednesday, May 22, 9:45 a.m. - 11:00 a.m.SESSION TOPIC: Assessing the Evidence for the Health Care Decision MakerAssessing Modeling Studies for Health Care DecisionsUBC Speaker: J. Jaime Caro, MDCM, FRCPC, FACP, Sr. VP Health Economics, UBC
ISPOR FORUMSESSION II - Tuesday, May 21, 6:15 p.m. - 7:15 p.m.Using Mixed Modes to Capture Patient-Reported Outcomes Data in Clinical TrialsModerator & Speaker: Sonya Eremenco, MA, Chair, ISPOR PRO Task Force: Good Research Practices for Mixed Modes to Collect PRO Data in Clinical Trials, and Dir. ePRO New Products, UBCSpeakers: Stephen Joel Coons, PhD, MS, MEd, Dir., Patient-Reported Outcomes Consortium, Critical Path Inst.; Jean Paty, PhD, Chief Scientist and Regulatory Advisor - Outcomes, ERT, Inc.
ISSUE PANELSESSION II - Tuesday, May 21, 11:00 a.m. - 12:00 p.m.IP8: Can Bayesian Adaptive CER Trial Designs Bridge the Needs of Patients, Payers, Regulators and ManufacturersModerator: Jack Ishak, PhD, Dir. and Sr. Research Scientist, Biostatistics and Epidemiology, UBCPanelists: Rachael Fleurence, PhD, Dir., Patient-Centered Outcomes Research Inst. (PCORI); Charles E. Barr, MD, MPH, Medical Dir., Head of Registries, Medical Affairs, Genentech; Robert T. O’Neill, PhD, Dir. Office of Biostatistics, CDER, U.S. FDA (Invited)
WORKSHOPSSESSION I - Monday, May 20, 5:00 p.m. - 6:00 p.m.W4: Capturing Patient and Observer Perspectives for Evaluating Treatments for Pediatric Mental Health Conditions - Opportunities and ChallengesDiscussion Leaders: Asha Hareendran, PhD, MA, Sr. Research Scientist, UBC; Juliana Setyawan, PharmD, MS, PhD, Shire Develop ment; Keith Saylor, PhD, Licensed Clinical Psychologist, NeuroScience Inc.; Elektra Papdopoulos, MD, MPH, CDER, U.S. FDA
W6: Best Practices and Promising Models for Patient Engagement in Patient-Centered ResearchDiscussion Leaders: Kathleen W. Wyrwich, PhD, Sr. Research Leader, UBC; Sue Sheridan, MIM, MBA, Dir. of Patient Engagement, Patient-Centered Outcomes Research Inst.; Theresa Mullin, PhD, Dir., Office of Planning and Informatics, CDER, U.S. FDA; Steven I. Blum, MBA, Dir., Health Economics and Outcomes Research, Forest Research Inst.
SESSION II - Tuesday, May 21, 3:45 p.m. - 4:45 p.m.W8: Animation of Discrete Event Simulation Models: Powerful Good Modeling Practice for Validation, Understanding and ReportingDiscussion Leaders: Jörgen Möller, MSc, VP, Health Economic Modeling, UBC; Luis Hernandez, MSc, Research Assoc., UBC; Jay Patrick Bae, PhD, Principal Research Scientist, Global Health Outcomes, Eli Lilly & Co.
SESSION III - Tuesday, May 21, 5:00 p.m. - 6:00 p.m.W19: The Utility of Mixed-Method Approaches to Evaluate the Content Validity of PRO MeasuresDiscussion Leaders: William R. Lenderking, PhD, Sr. Research Leader, UBC; Cheryl Coon, PhD, Dir., Psychometrics, RTI Health Solutions; Sheri E. Fehnel, PhD, VP, Patient-Reported Outcomes, RTI Health Solutions; Laurie B. Burke, RPh, MPH, Dir., Study Endpoints and Label Development, CDER, U.S. FDA
ISPOR 18th Annual International Meeting Presentations
May 18 - May 22, 2013
New Orleans, LA, USA
HEALTH ECONOMICS, OUTCOMES RESEARCH, MARKET ACCESS, DATA ANALYTICS, EPIDEMIOLOGY
SESSION IV - Wednesday, May 22, 1:45 p.m. - 2:45 p.m.W24: Novel Approaches in Early Planning for Relevant Evidence Generation to Support Health Care Decision MakingDiscussion Leaders: L. Clark Paramore, MSPH, Sr. Research Scientist, Health Economics, UBC; Rob Thwaites, MA, VP, Health Economics, UBC; Floortje E. van Nooten, MSc, Assoc. Dir., HEOR, Astellas; Jan E. Hansen, PhD, DrPH, VP, Global Health Outcomes, Allergan
SESSION V - Wednesday, May 22, 3:00 p.m. - 4:00 p.m.W27: Evaluations of Treatment Pathways in Oncology: Clinical and Economic Considerations and Modeling ApproachesDiscussion Leaders: Sonja Sorensen, MPH, Sr. Research Scientist, UBC; Feng Pan, PhD, Research Scientist, UBC; Jianming He, MS, MA, Assoc. Dir., Janssen Global Services; Kevin Knopf, MD, MPH, Medical Oncologist, California Pacific Medical Center
W28: Adapting AMCP Format-Based Dossiers: Evidence Guidelines for Specialty Pharmaceuticals, Companion Diagnostics, and Comparative Effectiveness ResearchDiscussion Leaders: Jeff Lee, PharmD, National Dir., Regional Scientific Services, Allergan; Bryan R. Luce, PhD, MBA, Sr. VP, Science Policy, UBC; Pete Penna, PharmD, President, Formulary Resources; David L. Veenstra, PhD, PharmD, Prof., Dept. of Pharmacy, Pharmaceutical Outcomes Research and Policy Program, Univ. of Washington
W31: The Use of Patient-Centered Health Care Information from Social Networking Sources in Health Economics and Outcomes ResearchDiscussion Leaders: Luke Boulanger, MA, MBA, Sr. Research Scientist and Sr. Dir., Health Economics, UBC; Doug McClure, MIM, Chief Technology Officer, Healthrageous; L. Clark Paramore, MSPH, Sr. Research Scientist, Health Economics, UBC
PODIUM PRESENTATIONSSESSION I - Monday, May 20, 2:15 p.m. - 3:15 p.m.CP3: Piecewise Modeling of Time-to-Event Data with Flexible Parameterization of Covariates and EffectsIshak J
SESSION II - Monday, May 20, 3:45 p.m. - 4:45 p.m.CE3: Advantages of Bayesian Adaptive Trials for Comparative Effectiveness Research (CER): “Re-Adapt”ing ALLHATBroglio K, Ishak J, Mullins CD, Connor J, Luce BR, Davis B
POSTER PRESENTATIONSSESSION I - Monday, May 20, 8:30 a.m. - 2:15 p.m.PMH53: Development of a Conceptual Disease Model to Inform Strategy to Evaluate Treatment Impact in Adolescents with Attention Deficit Hyperactivity Disorder (ADHD)Pokrzywinski R, Setyawan J, Khan S, Erder MH, Hodgkins P, Hareendran A
PRM66: Use of Common Data Model to Meaningfully Compare Treatment Patterns for Depression among Disparate DatabasesReisinger S, Powell G, Dreyfus B, Schneider G
PRM74: Modeling the Economic Implications of Alternative Treatments and Care Locations for Acute Bacterial Skin and Skin Structure Infections: Results from a Discrete Event SimulationRevankar N, Ward A, Kongnakorn T, Pelligra C, Fan W, LaPensee K
PRM120: Development of a New Patient-Reported Outcome (PRO) Instrument for Pulmonary Arterial Hypertension (PAH): The Pulmonary Arterial Hypertension-Symptoms and Impact (PAH-SYMPACT) QuestionnaireMcCollister D, Kummer S, Badesch DB, Filusch A, Hunsche E, Schuler R, Wiklund I, Peacock A
PRM144: An Adaptable Methodology for the Design, Implementation and Conduct of a Web-Based Survey Assessing Burden of Illness and Racial Differences: Case Study of Adult Females with AcneYeomans K, Kawata AK, Bassel M, Burk CT, Daniels SR, Wilcox TK
PRM206: Methodological Challenges in the Estimation of the Incidence Rate of Rare Diseases from Specialized Centers: Lessons Learned from a Study of Multicentric Castleman’s DiseaseTeltsch DY, Swain RS, Desrosiers M-P, Robinson Jr DW, Payne KA, Reynolds MW
PRM227: Considerations in the Application of Novel Statistical Methods for Crossover Adjustment in Trials of Cancer TreatmentsIshak J, Proskorovsky I, Korytowsky B, Sandin R
SESSION II - Monday, May 20, 3:45 p.m. - 7:45 p.m.PIH30: Cost Minimization Comparison of Darunavir + Ritonavir (DRV+RTV) to Lopinavir/Ritonavir (LPV/R) in HIV-1 Infected Treatment-Naive Women of Child Bearing Age (WOCBA)Desai K, Möller J, Simpson KN, Baran RW, Van de Steen O, Dietz B, Gooch K
PND10: Predicting the Long-Term Clinical Effectiveness of Daclizumab in Relapsing-Remitting Multiple Sclerosis: A New Modeling Framework Using Discrete Event SimulationGuo S, Hernandez L, Saint-Laurent Thibault C, Proskorovsky I, Philips GA
PND17: Study of Pseudobulbar Affect Symptoms in Veterans with Mild Traumatic Brain InjuryFonda JR, McGlinchey RE, Milberg WP, Rudolph JL, Hunt PR, Reynolds MW, Yonan C
SESSION III - Tuesday, May 21, 8:30 a.m. - 2:15 p.m.PDB18: Prevalence and Incidence of Acute Urogenital Conditions in a US Commercially-Insured PopulationLi Q, Wu N, Lee E, Sullivan P
PDB64: A Systematic Literature Review of Methodologies Used to Assess Medication Adherence in Patients with DiabetesClifford S, Perez-Nieves M, Skalicky A, Reaney M, Coyne KS
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APRIL 2013 EVIDENCE MATTERS
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PDB82: Assessment of Prevalence and Total Healthcare Costs of Associated Comorbidities among Commercially-Insured Type II DiabeticsChen SY, Alas V, Wu Y, Greene M, Biskupiak J
PDB95: Evaluation of Polypharmacy in Patients with Type 2 Diabetes Mellitus and Its Association with Medication Adherence and Healthcare CostsChen SY, Alas V, Lee YC, Greene M, Oderda G
PDB101: Patient Characteristics, Antidiabetic Medication Use, and Glycemic Control in Diabetic Nursing Home Residents with Moderate to Severe Chronic Kidney DiseaseWu N, Greene M, Oderda G
PDB102: Association of Severe Hypoglycemic Events and Hospital Readmission with Treatment Non-concordance to Guidelines and Prescribing Information in Hospitalized Patients with Type 2 Diabetes and Stage 3-5 Chronic Kidney DiseaseChen SY, Alas V, Greene M
PDB103: Oral Antidiabetic Use among Nursing Home Residents with Diabetes and Moderate to Severe Chronic Kidney DiseaseWu N, Yu X, Greene M, Oderda G
PSS17: A Conceptual Framework of Functional Reading Independence in Geographic AtrophyTschosik EA, Bressler NM, Colman S, Dolan C, Leidy NK, Oestreicher N, Sunness JS, Varma R, Kimel M
PUK19: Content Validity of the Actionable Bladder Symptom Screening Tool (ABSST) in Non-Diabetic Females with Overactive Bladder (OAB) and Urgency Urinary Incontinence (UUI)Nitti V, Dmochowski R, Chen WH, Signori M, Globe D, Wiklund I, Cardozo L
SESSION IV - Tuesday, May 21, 3:45 p.m. - 7:45 p.m.PHS3: Clinical and Economic Burden of Atrial Fibrillation in Medicare Beneficiaries with Acute Coronary SyndromeChen SY, Crivera C, Stokes M, Boulanger L, Schein J
PHS11: Economic Burden of Acute Urogenital Conditions among Type II Diabetes Patients and Non-Diabetics in the United StatesLi Q, Wu N, Lee E, Sullivan PW
PHS20: Direct Medical Costs of Diabetes Complications in the United StatesAlvarez P, Ward A, Chow W, Vo L, Martin S
PHS60: Association Between Comorbid Obesity with Health Status, Disability and Health-related Quality of Life in a Nationally Representative Type 2 Diabetes Mellitus PopulationChuang CC, Chen SY, Lee E, Sullivan PW
PHS98: Health Care Utilization and Costs Associated with Comorbid Obesity in Adults with Type 2 Diabetes Mellitus from a Nationally Representative US PopulationChuang CC, Chen SY, Lee E, Sullivan PW
PRS40: The EXACT in Idiopathic Pulmonary Fibrosis (IPF): Content Validity of an Existing Patient-Reported Outcome Measure for Use in a Rare DiseaseMurray L, Vernon M, O’Quinn S, Martinez F, Parker JM, Leidy NK
PRS46: A Cross-sectional, Hybrid, Patient Survey and Chart Review Study Design to Assess Patterns of Care, Patient Reported Outcomes, and Quality of life in Patients with Chronic Obstructive Pulmonary Disease (COPD)Desrosiers M-P, Payne KA, Boulanger L, Lordan N, Alas V, Zhang J, Massaro S
SESSION V - Wednesday, May 22, 8:30 a.m. - 2:45 p.m.PCV38: Hospitalization and Mortality in Medicare Heart Failure PatientsHunt PR, Veath BK, Tsintzos S, Burton ML, Mollenkopf SA
PCV42: Hospital Budget Impact of Readmission Penalties and Bundled Payments: Potential Impact of Coronary Stent PlatformsKansal AR, Stern S, Cohen D, Reifsnider O, Meskan T, Allocco D, Hale BC
PCV49: Economic Analysis of Stent Platforms: Cost-Effectiveness of the Platinum Chromium Promus Element Compared to Cobalt Chromium Promus/Xience vs Everolimus-Eluting StentsHale BC, Stern S, Kansal AR, Allocco D, Dawkins K, Stone G
PCV68: Economic Comparison of Hemostatic Agents in Cardiac SurgerySugarman R, Tackett S, Li-McLeod J, Kreuwel H, Alvarez P, Nasso G
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HEALTH ECONOMICS, OUTCOMES RESEARCH, MARKET ACCESS, DATA ANALYTICS, EPIDEMIOLOGY
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APRIL 2013 EvidEncE mattErs
RecentPresentations
31st Annual Dermatology Nurses’ Association Convention - 2013Apr 4 - Apr 7, 2013, New Orleans, LA, USA
POSTER PRESENTATIONRacial Differences in Treatment Satisfaction and Psychosocial Impact of Facial Acne in Adult FemalesTaylor SC, Alexis AF, Daniels SR, Kawata AK, Burk C, Yeomans K, Callender VD
AMCP Academy of Managed Care Pharmacy 25th Annual Meeting & Expo - 2013Apr 3 - Apr 5, 2013, San Diego, CA, USA
POSTER PRESENTATIONTreatment Patterns in Patients with HER2+ or ER/PR+ Metastatic Breast CancerTrask PC, Nordstrom BL, Ruiz Soto R, Fraeman KH, Garcia A
National Kidney Foundation 2013 Spring Clinical MeetingsApr 2 - Apr 6, 2013, Orlando, FL, USA
POSTER PRESENTATIONSGuideline Concordance of Treatments and its Association with Glycemic Control in Medicare-aged Type 2 Diabetes Mellitus Patients with Chronic Kidney DiseaseChen S, Lee Y, Alas V, Angalakuditi M
Treatment Concordance with Prescribing Information among Elderly Type 2 Diabetes Mellitus Patients with Chronic Kidney DiseaseChen S, Lee Y, Alas V, Angalakuditi M
Prevalence of Chronic Kidney Disease and Level of Glycemic Control in US Adults with Type 2 Diabetes MellitusChen S, Chuang C, Wu Y, Quon N
Prevalence of Undiagnosed Chronic Kidney Disease in Patients with Type 2 Diabetes MellitusChen S, Lee Y, Alas V, Angalakuditi M, Brixner D
Late Phase Drug Development World Americas 2013Mar 20 - Mar 21, 2013, Boston, MA, USA
SPEAKER PRESENTATIONInterpreting Change for Patient-Reported OutcomesKathleen W. Wyrwich, PhD, Sr. Research Leader, Outcomes Research, UBC; Joseph C. Cappelleri, PhD, Sr. Dir., Biostatistics, Pfizer
American Heart Association EPI/NPAM 2013Mar 19 - Mar 22, 2013, New Orleans, LA, USA
POSTER PRESENTATIONImpact of Comorbid Hypertension and Obesity on Glycemic Control in Adults with Type 2 Diabetes Mellitus from a National Representative US PopulationChen S, Chuang C, Wu Y, Quon N
AAN American Academy of Neurology 65th Annual MeetingMar 16 - Mar 23, 2013, San Diego, CA, USA
POSTER PRESENTATIONSCharacteristics and Burden of Subependymal Giant Cell Astrocytomas (SEGAs) in Patients with Tuberous Sclerosis Complex: Results of a Patient and Caregiver Survey in the USDunn DW, Rentz AM, Skalicky AM, Pashos CL, Liu Z, Pelletier C, Prestifilippo J, Nakagawa JA, Frost MD, Wheless JW
Correlations between MRI and Information Processing Speed in MS: Results of a Meta-AnalysisRao S, Martin A, Wissinger E, Huelin R, Kim E, Fahrbach K
Daclizumab High-Yield Process Treatment Reduced the Impact of Multiple Sclerosis Relapse on Health-Related Quality of Life (Results of the SELECT Trial)Vollmer T, Havrdova E, Selmaj K, Elkins J, Hass S, Guo S, Proskorovsky I, Phillips G
Responder Definition of the Multiple Sclerosis Impact Scale (MSIS-29) Physical Impact Scale for Patients with Physical WorseningPhillips G, Guo S, Elkins J, Putzki N, Altincatal A, Wyrwich K
ACC.13 American College of Cardiology 62nd Annual Scientific Session & Expo - 2013Mar 9 - Mar 11, 2013, San Francisco, CA, USA
POSTER PRESENTATIONReview of Literature on Health State Utilities Associated with Cardiovascular EventsMatza L, Chung K, Davies E, Gries K
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HealtH economics, outcomes ReseaRcH, maRket access, Data analytics, epiDemiology
TBI Traumatic Brain Injury Conference - 2013Mar 5 - Mar 7, 2013, Washington, DC, USA
POSTER PRESENTATIONStudy of Pseudobulbar Affect Symptoms in Veterans with Mild Traumatic Brain InjuryFonda JR, McGlinchey RE, Milberg WP, Rudolph JL, Hunt PR, Reynolds MW, Yonan C
DIA 25th Annual EuroMeeting Amsterdam - 2013Mar 4 - Mar 6, 2013, Amsterdam, Netherlands
POSTER PRESENTATIONHow Should Benefit Risk Assessment be Incorporated across the Product LifecycleMarsh K, Neasham D
AAD 2013 Academy of Dermatology Annual MeetingMar 1 - Mar 5, 2013, Miami Beach, FL, USA
POSTER PRESENTATIONSAcne Vulgaris in Adult Females: Racial Differences in Clinical Characteristics, Perceptions, and Behaviors Related to Facial AcneCallender VD, Alexis AF, Kawata AK, Daniels SR, Yeomans K, Burk C, Taylor SC
Characteristics and Burden of Skin Lesions in Patients with Tuberous Sclerosis Complex: Results of a Patient and Caregiver Survey in the USWheless JW, Rentz AM, Skalicky AM, Pashos CL, Liu Z, Pelletier C, Prestifilippo J, Nakagawa JA, Dunn DW, Frost MD
Improvement in Patient Reported Symptoms and Health Related Quality of Life (HRQoL) Associated with Achieving Psoriasis Area and Severity Index (PASI) 100Revicki DA, Jin Y, Hsieh R, Erondu N, Viswanathan HN
Evidence 2013Feb 19 - Feb 20, 2013, London, UK
SPEAKER PRESENTATIONSEvidence Surveillance as the Necessary Step to Appropriate Design and Execution of Real World StudiesAlison Martin, MB ChB, BSc, Sr. Research Scientist, Evidence Review and Synthesis, UBC; Radek Wasiak, PhD, MA, MSc, European Dir. and Sr. Research Scientist, Health Economics, UBC
How to Support Product Value through PROsIngela Wiklund, PhD, Sr. Research Leader, UBC
PROs for Labeling and Promotional Claims: FDA and EMA ConsiderationsMargaret K. Vernon, PhD, Sr. Research Scientist, EU Director, Outcomes Research, UBC
ISC International Stroke Conference - 2013Feb 6 - Feb 7, 2013, Honolulu, HI, USA
POSTER PRESENTATIONApplying ARISTOTLE Trial to Actual PracticeAmin A, Stokes M, Gatt E, Wu N, Makenbaeva D, Wiederkehr D, Boulanger L
EAHAD European Association for Haemophilia and Allied Disorders Annual Congress - 2013Feb 6 - Feb 8, 2013, Warsaw, Poland
POSTER PRESENTATIONPatient and Caregiver Preferences for Treatment Characteristics of Severe Haemophilia ARentz A, Pocoski J, Gries K, Mathew P, Sasne R
GI Cancers Symposium 2013Jan 24 - Jan 26, 2013, San Francisco, CA, USA
POSTER PRESENTATIONCost-effectiveness of Regorafenib for Pretreated Metastatic Colorectal Cancer Patients in the United StatesSeal BS, Ozer-Stillman I, Whalen JD, Ambavane A, Yaldo A, Pawar V
ODAC 2013 Orlando Dermatology Aesthetic and Clinical ConferenceJan 18 - Jan 21, 2013, Orlando, FL, USA
POSTER PRESENTATIONAcne Vulgaris in Adult Females: Healthcare Resource Utilization for Treatment of Facial AcneBaldwin HE, Daniels SR, Kawata AK, Burk CT, Wilcox TK, Tanghetti EA
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APRIL 2013 EvidEncE mattErs
RecentPublications
Ahmed S, Berzon RA, Revicki DA, Lenderking WR, Moinpour CM, Basche E, Reeve BB, Wu AW. The Use of Patient-reported Outcomes (PRO) Within Comparative Effectiveness Research: Impli-cations for Clinical Practice and Health Care Policy. Med Care. 2012 Dec; 50(12):1060-1070.
Albrecht H, Vernon M, Solomon G. Patient-reported Outcomes to Assess the Efficacy of Extended-release Guaifenesin for the Treatment of Acute Respiratory Tract Infection Symptoms. Respir Res. 2012 Dec 27; 13(1):118.
Bime C, Wei CY, Holbrook JT, Sockrider MM, Revicki DA, Wise RA. Asthma Symptom Utility Index: Reliability, Validity, Responsiveness, and the Minimal Important Difference in Adult Asth-matic Patients. J Allergy Clin Immunol. 2012 Nov; 130(5):1078-1084. [Epub 2012 Sep 29]
Blumenfeld AM, Bloudek LM, Becker WJ, Buse DC, Varon SF, Maglinte GA, Wilcox TK, Kawata AK, Lipton RB. Patterns of Use and Reasons for Discontinuation of Prophylactic Medications for Episodic Migraine and Chronic Migraine: Results from the Second International Burden of Migraine Study (IBMS-II). Headache. 2013 Jan 4 [Epub ahead of print]
Bradley JM, Blume SW, Balp MM, Honeybourne D, Elborn JS. Quality of Life and Healthcare Utilisa-tion in Cystic Fibrosis: A Multicentre UK Study. Eur Respir J. 2013 Mar; 41(3):571-577. [Epub 2012 Jul 26]
Brandt BA, Angun C, Coyne KS, Doshi S, Baven-dam T, Kopp ZS. LUTS Patient Reported Outcomes Tool: Linguistic Validation in 10 European Lan-guages. Neurourol Urodyn. 2013 Jan; 32(1):75-81. [Epub 2012 Jun 5]
Brown RE, Stern S, Dhanasiri S, Schey S. Lenalido-mide for Multiple Myeloma: Cost-effectiveness in Patients with One Prior Therapy in England and Wales. Eur J Health Econ. 2012 May 10 [Epub ahead of print]
Brundage M, Blazeby J, Revicki D, Bass B, de Vet H, Duffy H, Efficace F, King M, Lam CL, Moher D, Scott J, Sloan J, Snyder C, Yount S, Calvert M. Patient-reported Outcomes in Randomized Clinical Trials: Development of ISOQOL Report-ing Standards. Qual Life Res. 2012 Sep 18 [Epub ahead of print]
Byrnes M, Travers K, Burns M, Sapra S. A Systematic Literature Review Examining Soluble and Cellular Biomarkers in HIV Patients Receiving Antiretroviral Therapy. J Int AIDS Soc. 2012 Nov 11; 15(6):18172.
Calvert M, Blazeby J, Altman DG, Revicki DA, Moher D, Brundage MD; for the CONSORT PRO Group. Reporting of Patient-Reported Outcomes in Randomized Trials: The CONSORT PRO Extension. JAMA. 2013 Feb 27; 309(8):814-822.
Caro JJ. How Cost-effective are Drugs and Devices Used in Cardiology? Eur Heart J. 2012 Oct; 33(19):2372-2373.
Caro JJ. How Relevant is Cost-effectiveness Any-way? Eur Heart J. 2013 Jan; 34(4):245-246.
Chen SY, Lee YC, Alas V, Greene M, Brixner D. Outcomes Associated with Concordance of Oral Antidiabetic Drug Treatments to Prescribing Infor-mation in Patients With Type 2 Diabetes Mellitus and Chronic Kidney Disease. J Med Econ. 2013 Feb 12 [Epub ahead of print]
Chen SY, Vanderpoel J, Mody S, Nelson WW, Schein J, Rao P, Boulanger L. Caregiver Assistance Among Medicare Beneficiaries With Atrial Fibril-lation and Factors Associated With Anticoagulant Treatment. Am J Geriatr Pharmacother. 2012 Oct; 10(5):273-83. [Epub 2012 Sep 12]
Chen SY, Wu N, Boulanger L, Sacco P. Anti-epileptic Drug Treatment Patterns and Economic Burden of Commercially-insured Patients with Refractory Epilepsy with Partial Onset Seizures in the United States. J Med Econ. 2013; 16(2):240-248. [Epub 2012 Nov 28]
Chen X, Bailleux F, Desai K, Qin L, Dunning AJ. A Threshold Method for Immunological Correlates of Protection. BMC Med Res Methodol. 2013 Mar 1; 13(1):29 [Epub ahead of print]
Coleman CN, Adams S, Adrianopoli C, Ansari A, Bader JL, Buddemeier B, Caro JJ, Casagrande R, et al. Medical Planning and Response for a Nuclear Detonation: a Practical Guide. Biosecur Bioterror. 2012 Dec; 10(4):346-371.
Coleman CN, Hrdina C, Casagrande R, Cliffer KD, Mansoura MK, Nystrom S, Hatchett R, Caro JJ, Knebel AR, Wallace KS, Adams SA. User-Managed Inventory: An Approach to Forward-Deployment of Urgently Needed Medical Countermeasures for Mass-Casualty and Terrorism Incidents. Disaster Med Public Health Prep. 2012 Dec; 6(4):408-414.
Coyne KS, Margolis MK, Cappelleri JC, Hsieh R, Noyes Essex M, Park PW, Joshi AV. Prevalence of Gastroprotective Agent (GPA) Use in Adults with Arthritis in the United States. Curr Med Res Opin. 2013 Feb 28 [Epub ahead of print]
Coyne KS, Sexton CC, Bell JA, Thompson CL, Dmochowski R, Bavendam T, Chen CI, Quentin Clemens J. The Prevalence of Lower Urinary Tract Symptoms (LUTS) and Overactive Bladder (OAB) by Racial/Ethnic Group and Age: Results from OAB-POLL. Neurourol Urodyn. 2012 Jul 27 [Epub ahead of print]
De Cock E, Dellanna F, Khellaf K, Klatko W, Maduell F, Raluy-Callado M, Villa G. Time Savings Associated with C.E.R.A. Once Monthly: a Time-and-Motion Study in Haemodialysis Centres in Five European Countries. J Med Econ. 2013 Feb 12 [Epub ahead of print]
Erder MH, Wilcox TK, Chen WH, O’Quinn S, Setyawan J, Saxton J. A New Measure of Caregiver Burden in Alzheimer’s Disease: The Caregiver-Per-ceived Burden Questionnaire. Am J Alzheimers Dis Other Demen. 2012 Nov; 27(7):474-482. [Epub 2012 Aug 16]
Friedman M, Spalding J, Kothari S, Wu Y, Gatt E, Boulanger L. Myocardial Perfusion Imaging Laboratory Efficiency with the Use of Regadenoson Compared to Adenosine and Dipyridamole. J Med Econ. 2013 Jan 30 [Epub ahead of print]
Gandhi PK, Kenzik KM, Thompson LA, DeWalt DA, Revicki DA, Shenkman EA, Huang IC. Exploring Factors Influencing Asthma Control and Asthma-specific Health-related Quality of Life among Children. Respir Res. 2013 Feb 23; 14(1):26 [Epub ahead of print]
Ganz ML, Wu N, Rawn J, Pashos CL, Strandberg-Larsen M. Clinical and Economic Outcomes Associated with Blood Transfusions among Elderly Americans Following Coronary Artery Bypass Graft Surgery Requiring Cardiopulmonary Bypass. Blood Transfus. 2013 Feb 6:1-10 [Epub ahead of print]
Gartemann J, Caffrey E, Hadker N, Crean S, Creed GM, Rausch C. Nurse Workload in Implementing a Tight Glycaemic Control Protocol in a UK Hospital: A Pilot Time-in-motion Study. Nurs Crit Care. 2012 Nov; 17(6):279-84. [Epub 2012 May 2]
Gelhorn H, Merikle E, Krishnan S, Nemes L, Leissinger C, Valentino L. Physician Preferences for Medication Attributes for the Prophylactic Treat-ment of Patients with Severe Haemophilia A with Inhibitors to Factor VIII. Haemophilia. 2013 Jan; 19(1):119-25. [Epub 2012 Sep 25]
Gelhorn HL, Stringer SM, Brooks A, Thomp-son C, Monz BU, Boye KS, Hach T, Lund SS, Palencia R. Preferences for Medication Attributes Among Patients with Type 2 Diabetes Mellitus in the United Kingdom. Diabetes Obes Metab. 2013 Mar 6 [Epub ahead of print]
Guo S, Getsios D, Hernandez L, Cho K, Lawler E, Altincatal A, Lanes S, Blankenburg M. Flor-betaben PET in the Early Diagnosis of Alzheimer’s Disease: A Discrete Event Simulation to Explore its Potential Value and Key Data Gaps. Int J Alzheim-ers Dis. 2012; 2012:548157. [Epub 2012 Dec 26]
Hass B, Pooley J, Feuring M, Suvarna V, Har-rington AE. Health Technology Assessment and its Role in the Future Development of the Indian Healthcare Sector. Perspect Clin Res. 2012 Apr; 3(2):66-72.
Hass B, Pooley J, Harrington AE, Clemens A, Feuring M. Treatment of Venous Thromboembo-lism - Effects of Different Therapeutic Strategies on Bleeding and Recurrence Rates and Considerations for Future Anticoagulant Management. Thromb J. 2012 Dec 31; 10(1):24.
Howard K, Berry P, Petrillo J, Wiklund I, Roberts L, Watkins M, Crim C, Wilcox T. Development of the Shortness of Breath with Daily Activities Questionnaire (SOBDA). Value Health. 2012 Dec; 15(8):1042-50. [Epub 2012 Oct 4]
Igarashi A, Kuwabara H, Fahrbach K, Schenkel B. Cost Efficacy Comparison of Biological Therapies for Patients with Moderate to Severe Psoriasis in Japan. J Dermatolog Treat. 2012 May 28 [Epub ahead of print]
Ireland AM, Wiklund I, Hsieh R, Dale P, O’Rourke E. An Electronic Diary Is Shown to Be More Reliable than a Paper Diary: Results from a Randomized Crossover Study in Patients with Persistent Asthma. J Asthma. 2012 Nov; 49(9):952-60. [Epub 2012 Oct 4]
Ishak KJ, Stolar M, Hu MY, Alvarez P, Wang Y, Getsios D, Williams GC. Accounting for the Re-lationship between Per Diem Cost and LOS When Estimating Hospitalization Costs. BMC Health Serv Res. 2012 Dec 1; 12:439.
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HealtH economics, outcomes ReseaRcH, maRket access, Data analytics, epiDemiology
Kadambi A, Leipold RJ, Kansal AR, Sorensen S, Getsios D. Inclusion of Compliance and Persistence in Economic Models: Past, Present and Future. Appl Health Econ Health Policy. 2012 Nov 1; 10(6):365-379.
Kansal AR, Sharma M, Bradley-Kennedy C, Clem-ens A, Monz BU, Peng S, Roskell N, Sorensen SV. Dabigatran Versus Rivaroxaban for the Preven-tion of Stroke and Systemic Embolism in Atrial Fibrillation in Canada. Comparative Efficacy and Cost-effectiveness. Thromb Haemost. 2012 Sep 27; 108(4):672-82. [Epub 2012 Aug 17]
Khalid JM, Fox KM, Globe G, Maguire A, Chau D. Treatment Patterns and Therapy Effectiveness in Psoriasis Patients Initiating Biologic Therapy in England. J Dermatolog Treat. 2013 Jan 22. [Epub ahead of print]
Kim J, Chung H, Amtmann D, Revicki DA, Cook KF. Measurement Invariance of the PROMIS Pain Interference Item Bank across Community and Clinical Samples. Qual Life Res. 2012 May 3 [Epub ahead of print]
Kleinman L, Buysse DJ, Harding G, Lichstein K, Kalsekar A, Roth T. Patient-reported Outcomes in Insomnia: Development of a Conceptual Frame-work and Endpoint Model. Behav Sleep Med. 2013 Jan; 11(1):23-36.
Larsen L, Coyne K, Chwalisz K. Validation of the Menstrual Pictogram in Women With Leiomyo-mata Associated With Heavy Menstrual Bleeding. Reprod Sci. 2012 Nov 27 [Epub ahead of print]
Marsh K, Caro JJ, Muszbek N. Does the Future Belong to MCDA? ISPOR Connections. 2012 Nov/Dec; 18(6):9-11.
Martin AL, Huelin R, Wilson D, Foster TS, Mould JF. A Systematic Review Assessing the Economic Impact of Sildenafil Citrate (Viagra(®) ) in the Treatment of Erectile Dysfunction. J Sex Med. 2013 Jan 24 [Epub ahead of print]
Matza LS, Chung K, Van Brunt K, Brazier JE, Braun A, Currie B, Palsgrove A, Davies E, Body JJ. Health State Utilities for Skeletal-related Events Secondary to Bone Metastases. Eur J Health Econ. 2013 Jan 25 [Epub ahead of print]
Matza LS, Phillips GA, Revicki DA, Ascher-Svanum H, Malley KG, Palsgrove AC, Faries DE, Stauffer V, Kinon BJ, George Awad A, Keefe RS, Naber D. Validation of a Clinician Questionnaire to Assess Reasons for Antipsychotic Discontinu-ation and Continuation among Patients with Schizophrenia. Psychiatry Res. 2012 Dec 30; 200(2-3):835-842. [Epub 2012 Jul 28]
Matza LS, Wyrwich KW, Phillips GA, Murray LT, Malley KG, Revicki DA. The Fatigue Associated with Depression Questionnaire (FAsD): Respon-siveness and Responder Definition. Qual Life Res. 2013 Mar; 22(2):351-60. [Epub 2012 Mar 9]
Mercaldi CJ, Lanes SF. Ultrasound Guidance Decreases Complications and Improves the Cost of Care Among Patients Undergoing Thoracentesis and Paracentesis. Chest. 2013 Feb 1; 143(2):532-8.
Mercaldi CJ, Siu K, Sander SD, Walker DR, Wu Y, Li Q, Wu N. Long-Term Costs of Ischemic Stroke and Major Bleeding Events among Medicare Patients with Nonvalvular Atrial Fibrillation. Cardiol Res Pract. 2012; 2012:645469. [Epub 2012 Oct 2]
Nafees B, van Hanswijck de Jonge P, Stull D, Pascoe K, Price M, Clarke A, Turkington D. Reliability and Validity of the Personal and Social Performance scale in Patients with Schizophrenia. Schizophr Res. 2012 Sep; 140(1-3):71-76. [Epub 2012 Jun 30]
Naik RK, Rentz AM, Foster CS, Lightman S, Belfort R, Lowder C, Whitcup SM, Kowalski JW, Revicki DA. Normative Comparison of Patient-reported Outcomes in Patients with Noninfectious Uveitis. JAMA Ophthalmol. 2013 Feb 1; 131(2):219-25.
Nalysnyk L, Cid-Ruzafa J, Rotella P, Esser D. Incidence and Prevalence of Idiopathic Pulmonary Fibrosis: Review of the Literature. Eur Respir Rev. 2012 Dec 1; 21(126):355-361.
Neville SE, Boye KS, Montgomery WS, Iwamoto K, Okamura M, Hayes RP. Diabetes in Japan: a Review of Disease Burden and Approaches to Treatment. Diabetes Metab Res Rev. 2009 Nov; 25(8):705-716.
Ngamphaiboon J, Kongnakorn T, Detzel P, Sirisomboonwong K, Wasiak R. Direct Medical Costs Associated with Atopic Diseases among Young Children in Thailand. J Med Econ. 2012; 15(6):1025-1035. [Epub 2012 Jun 14]
Orr P, Rentz AM, Margolis MK, Revicki DA, Dolan CM, Colman S, Fine JT, Bressler N. Author Response: Validation of the National Eye Institute Visual Function Questionnaire-25 (NEIVFQ-25) in Age-Related Macular Degeneration. Invest Oph-thalmol Vis Sci. 2012 Dec 3; 53(13).
Pleil AM, Kimel M, McCormack J, Rajicic N, Hey-Hadavi J. Psychometric Assessment of the Injection Pen Assessment Questionnaire (IPAQ): Measuring Ease of Use and Preference with Injection Pens for Human Growth Hormone. Health Qual Life Outcomes. 2012 Oct 9;10(1):126.
Reeve BB, Wyrwich KW, Wu AW, Velikova G, Terwee CB, Snyder CF, Schwartz C, Revicki DA, Moinpour CM, McLeod LD, Lyons JC, Lenderking WR, Hinds PS, Hays RD, Greenhalgh J, Gershon R, Feeny D, Fayers PM, Cella D, Brundage M, Ahmed S, Aaronson NK, Butt Z. ISOQOL Recommends Minimum Standards for Patient-reported Outcome Measures Used in Patient-centered Outcomes and Comparative Effectiveness Research. Qual Life Res. 2013 Jan 4 [Epub ahead of print]
Revicki DA, Brandenburg NA, Muus P, Yu R, Knight R, Fenaux P. Health-related Quality of Life Outcomes of Lenalidomide in Transfusion-depen-dent Patients with Low- or Intermediate-1-risk Myelodysplastic Syndromes with a Chromosome 5q Deletion: Results from a Randomized Clinical Trial. Leuk Res. 2013 Mar; 37(3):259-265
Revicki DA, Jin Y, Wilson HD, Chau D, Viswa-nathan HN. Reliability and Validity of the Psoriasis Symptom Inventory in Patients with Moderate to Severe Psoriasis. J Dermatolog Treat. 2013 Jan 22 [Epub ahead of print]
Safikhani S, Sundaram M, Bao Y, Mulani P, Revicki DA. Qualitative Assessment of the Content Validity of the Dermatology Life Quality Index in Patients with Moderate to Severe Psoriasis. J Dermatolog Treat. 2013 Feb; 24(1):50-9. [Epub 2011 Nov 10]
Sexton CC, Gelhorn H, Bell J, Classi P. The Co-occurrence of Reading Disorder and ADHD: Epidemiology, Treatment, Psychosocial Impact, and Economic Burden. J Learn Disabil. 2012 Nov; 45(6):538-564. [Epub 2011 Jul 14]
Travers K, Martin A, Khankhel Z, Boye KS, Lee LJ. Burden and Management of Chronic Kidney Disease in Japan: Systematic Review of the Litera-ture. Int J Nephrol Renovasc Dis. 2013; 6:1-13. [Epub 2013 Jan 3]
Van Nooten F, Caro JJ. Use of Relative Effective-ness Information in Reimbursement and Pricing Decisions in Europe. J Compar Effect Res. 2013 Jan; 2(1):33-44.
Van Nooten F, Stern S, Braunstahl GJ, Thomp-son C, Groot M, Brown RE. Cost-effectiveness of Omalizumab for Uncontrolled Allergic Asthma in the Netherlands. J Med Econ. 2013; 16(3):342-348. [Epub 2012 Dec 18]
Vernon MK, Wiklund I, Bell JA, Dale P, Chapman KR. What Do We Know about Asthma Triggers? A Review of the Literature. J Asthma. 2012 Dec; 49(10):991-998.
Waxman A, Chen SY, Boulanger L, Watson JA, Golden G. Factors Associated with Adherence to Phosphodiesterase Type 5 Inhibitors for the Treat-ment of Pulmonary Arterial Hypertension. J Med Econ. 2013; 16(2):298-306. [Epub 2012 Dec 12]
Wiklund I, Holmstrom S, Stoker M, Wyrwich KW, Devine M. Are Treatment Benefits in Neuropathic Pain Reflected in the Self Assessment of Treatment Questionnaire? Health Qual Life Outcomes. 2013 Jan 18; 11(1):8.
Wiklund I, Raluy-Callado M, Stull DE, Jangelind Y, Whiteman DA, Chen WH. The Hunter Syndrome-Functional Outcomes for Clinical Understanding Scale (HS-FOCUS) Questionnaire: Evaluation of Measurement Properties. Qual Life Res. 2012 May 19 [Epub ahead of print]
Wong K, Boulanger L, Smalarz A, Wu N, Fraser K, Wogen J. Impact of Care Management Pro-cesses and Integration of Care on Blood Pressure Control in Diabetes. BMC Fam Pract. 2013 Feb 27; 14(1):30 [Epub ahead of print]
Wu N, Aagren M, Boulanger L, Friedman M, Wilkey K. Assessing Achievement and Mainte-nance of Glycemic Control by Patients Initiating Basal Insulin. Curr Med Res Opin. 2012 Oct; 28(10):1647-56.[Epub 2012 Sep 2]
Wyrwich KW, Norquist JM, Lenderking WR, Acaster S, and the Industry Advisory Committee of International Society for Quality of Life Research (ISOQOL). Methods for Interpreting Change over Time in Patient-reported Outcome Measures. Qual Life Res. 2012 Apr 17 [Epub ahead of print]
Yong H, Foody J, Linong J, Dong Z, Wang Y, Ma L, Meng HJ, Shiff S, Dayi H. A Systematic Literature Review of Risk Factors for Stroke in China. Cardiol Rev. 2013 Mar; 21(2):77-93.
Young AE, Wasiak R, Gross DP. Recurrence of Work-Related Low Back Injuries: The Association between Patient Self-Report and Indemnity-Based Assessments. WorkSafeBC.com. Research Project Number: RS2007-OG02; 2012 Nov 6.
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UpcomingPresentations
ECCMID 23rd European Congress of Clinical Microbiology and Infectious Diseases - 2013Apr 27 - Apr 30, 2013, Berlin, GermanyPOSTER PRESENTATIONSEconomic Implications of Alternative Treatments and Care Locations for Acute Bacterial Skin and Skin Structure Infections: Results from a Discrete Event SimulationRevankar N, Ward A, Kongnakorn T, Pelligra C, Fan W, LaPensee K
Incidence and Streptococcus Pneumoniae Serotype Distribution Associated with Reportable Meningitis in Norway, 2007-2009Gray S, Lambrelli D, Eriksson D, Munson S, Wasiak R, Raluy M
The Clinical Burden of Hospitalised All-Cause and Pneumococcal Meningitis and Septicaemia in the Lombardia and Puglia Regions of Italy, 2007-2009Gray S, Lambrelli D, Wasiak R, Veronesi C, Raluy M, Munson S, Buda S, Icardi G
NADNP National Academy of Dermatology Nurse Practitioners Conference - 2013 May 14 - 18, 2013, Clearwater Beach, FL, USAPOSTER PRESENTATIONSAcne Vulgaris in Adult Females: Healthcare Resource Utilization for Treatment of Facial AcneTanghetti EA, Daniels SR, Kawata AK, Burk CT, Wilcox TK, Baldwin HE
Acne Vulgaris in Adult Females: Racial Differences in Clinical Characteristics, Perceptions, and Behaviors Relevant to Facial AcneTaylor SC, Alexis AF, Kawata AK, Daniels SR, Yeomans K, Burk C, Callender VD
QCOR 2013 - Quality of Care and Outcomes Research Scientific SessionsMay 15 - May 17, 2013, Baltimore, MD, USAPOSTER PRESENTATIONSProjected Number of HF Admissions Per Heart Failure Patient In The Medicare PopulationHunt PR, Wu N, Veath BK, Tsintzos S, Burton ML, Mollenkopf SA
Heart Failure and Outcomes after Acute Coronary Syndrome in a Representative Medicare PopulationChen SY, Crivera C, Stokes M, Boulanger L, Schein J
IUGA 2013May 28 - Jun 1, 2013, Dublin, IrelandPOSTER PRESENTATIONValidation of a Bladder Symptom Screening Tool in a Female Gynecological Population with Overactive Bladder (OAB) and Urinary Urgency Incontinence (UUI)Cardozo L, Noblett K, Staskin D, Chen WH, Currie B, Wiklund I, Globe D, Dmochowski R, Signori M, Macdiarmid S, Nitti VW
Real World Data Europe 2013Jun 5 - Jun 6, 2013, London, UK
CONFERENCE CHAIR - DAY ONERob Thwaites, MA, VP Health Economics, UBC
SPEAKER PRESENTATIONThe Real World Data Conundrum Facing BioPharma in the U.S.’s Real WorldBryan R. Luce, PhD, MBA, Sr. VP Science Policy, UBC
4th World Congress on ADHD - 2013Jun 6 - Jun 9 2013, Milan, ItalyPOSTER PRESENTATIONMeasuring Functioning in Adolescents with ADHD. Do Existing Tools Reflect the Adolescents’ Perspective?Setyawan J, Hareendran A, Erder MH, Hodgkins P, Trundell D, Pokrzywinski R
AcademyHealth 2013 Annual Research Meeting (ARM) Jun 23 - Jun 25, 2013, Baltimore, MD, USASESSION PRESENTATIONThe Application of Bayesian Adaptive Designs for CER TrialsChair: Bryan R. Luce, PhD, MBA, Sr. VP Science Policy, UBC
ADA American Diabetes Association Annual Scientific Session - 2013Jun 21 - Jun 25, 2013, Chicago, IL, USAPOSTER PRESENTATIONPsychometric Properties of the Hypoglycemia Perspec-tives Questionnaire (HPQ) in Type 2 Diabetes MellitusKawata A, Ong SH, Therapontos C, Mavrogenis P, Kulich K, Chen WH, Coyne K
