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Adaptive Design

Ana Ruiz

Paul Bycott

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Definition

•Clinical study design that uses accumulating data to decide on how to modify aspects of the study as it continues, without undermining the validity and integrity of the trial.

•Goal: learn from the accumulating data and to apply what is learned as quickly as possible.

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Advantages and Disadvantages

• Potential to speed up the process of drug development

• Allocate resources more efficiently without lowering scientific and regulatory standards

• The basis for regulatory decision-making may be improved

• Breaking the blind early can lead to problems with dissemination of study results and the study population might change between the early and late stages of drug testing.

• Often less statistically efficient than fixed plans

• Regulators are very disturbed by changing scientific hypotheses

• Statistical methods for the design and analysis are technically and computationally more complex than those associated with conventional designs.

• Software availability

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Implications

• The statistical methods control the pre-specified type I error

• Correct estimates and confidence intervals for the treatment effect are available

• Methods for the assessment of homogeneity of results from different stages are pre-planned

• Require rapid data collection to fully take advantage of the efficiencies they offer

• Is there sufficient statistical expertise, software and time to evaluate and document the adaptive design of interest?

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•“ Adaptive designs should not be seen as means to alleviate the burden of rigorous planning of a clinical trial. Instead, adaptive designs would be best utilized as a tool for planning clinical trials in areas where it is necessary to cope with difficult experimental situations”•Reflection paper on methodological issues in confirmatory clinical trials planned with adaptative design. Committee for Medicinal Products for

Human Use. (CHMP). Emea, october 2007. CHMP/EWP/2459/02 •Adaptation is a design feature aimed to enhance the trial, not a remedy for inadequate planning

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Summary of key positions from EMEA/EFPIA workshop on adaptive designs

in confirmatory trials, December 2007Agreement

• Cost of R&D is a public health issue• Growing patient pressure• Interim analysis can be ethical/mandatory• AD is not a rescue remedy• Control of type I error is feasible• Early stopping will reduce totality of evidence• Some reassuring examples of seamless designs exist• There is a need for replication• Trial integrity is the primary goal• Risk of operational bias is obvious• Justification for sponsor involvement is not

impossible• AD means improved transparency• Price to pay: multiplicity and increased awareness of

heterogeneity• Industry and regulators need to work together

Disagreement

• Cost of R&D as main driver for ADNeed to save time per se

• Complete blurring of exploratory and confirmatory phases

• Sponsor involvement should be the rule• How to quantify (and tolerate) heterogeneity

Members: EU and US regulators, academia and industry experts

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Applications in Oncology

• Adaptive Dose finding

• Sample size re-estimation

• Seamless phase II/III designs

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Adaptive Dose Finding

• Conventional studies: A few doses of a drug in a fixed parallel group study requiring large sizes to estimate pairwise differences

• Adaptive designs provide opportunities to characterize the dose response more fully and efficiently. Sponsors can also improve patient care within an adaptive design trial by implementing early stopping rules and adaptive treatment allocation schemes limiting patient exposure to unsafe or ineffective doses.

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Huang et al.

• CRM: Continual reassessment method. Bayesian parametric method in which single-patient cohorts are successively assigned to the current posterior estimates of the MTD

• LMH-CRM: Low, Medium and High CRM. Instead of administering the same dose to all patients in the same cohort, multiple doses are assigned to them initially (low, medium and high). This method will treat fewer patients at low, possibly ineffective dose levels when the initial levels are far below the true MTD.

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Simulation of 24 patients treated sequentially in 8 cohorts

•First cohort, concern about the high dose: restriction to the escalation of H-doseXm+1,H=min{Xm+1,H, XmH+d}After that:Xm+1,l=min{Xm+1,l, Xm+1,H}

x:dose levelm: cohortsd: number of levels allowed for escalation

– 1:slow LMH-CRM– 2:moderate LMH-CRM– 3:fast LMH-CRM

Target probablity of DLT:0.33. dose level 6 true MTD. Set 30%, 50% and 70% as the post quantiles of the MTD

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Performance analysis

• 24 patients• 10 dose levels• 6 scenarios with different values for true tox

probabilities• MTD defined as dose level at which 33% of

patients would experience DLT• 1000 simulations• Power function used as dose-toxicity model

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Summary

Pros

• Addresses the ethical demands to assign fewer patients to mild doses that may not be therapeutic

• Controls overall toxicity rate

• Time efficient shortening trial duration

Cons

• Statistical methods for the design and analysis are technically and computationally more complex than those associated with conventional designs.

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Sample size re-estimation

• Assumptions need to be made based upon the available knowledge at the time of the trial design.– Number of patients– Target number of events– Follow-up period

• Adaptive designs allow within the setting of the trial itself correctness of the assumptions made in the interest of assuring that trials are neither overly large, nor too limited to adequately address their objectives. (revision of the amount of information collected as long as this can be done in a manner which does not compromise the integrity of the trial or interpretability of the results

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Adaptive Statistical Analysis Following Sample Size Modification Based on Interim

Review of Effect Size (James Hung)

• Adaptive design for sample size re-estimation based partly on the effect size observed at an interim analysis .

• The performance of this strategy is compared with a fix maximum sample size design properly adjusted in anticipation of the possible sample size adjustment

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Methodology

• Comparing the means of a continuous response for two randomized treatment groups 1 and 2, being i the response mean and the standard deviation.

• Let =(1- 2)/ , H0: =0 vs. H1: >0• Two samples Z-test if is known. The adaptive

test would be U• t-test if is unknown • Max per group sample size: n, initially planned to

detect = at a level of significance and with power 1-, where is the postulated effect size

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• Designs: D0, E1 and E2– D0: a fixed sample size design with plan of sample size n to detect =– E1: initial sample size=D0 but with sample adjustment: 0.5, accept H0 and stop trial

if 0.5-0.27. Otherwise determine new SZ, m such as *=0.6 the conditional power achieves 90%. In addition, n m Nmax=4n

– E2: a fixed sample size design by planning sample size n* to detect 0.6 at =0.025 and =0.10

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Summary

Pros• Sample size re-estimation allows for

some flexibility in the misspecification of the design assumptions used to size the trial initially and for changes that may occur over time

• The statistical methods control the pre-specified type I error ( more complex methods of analysis)

• Sample size adaptation adds flexibility to the fixed sample size design, regardless of whether the resulting is optimal or not

Cons• Optimal Timing for re-assessment

needs research (sufficient accumulated information is needed)

• Breaking the blind early can lead to problems with dissemination of study results and the study population might change between the early and late stages of drug testing.

• Methods for the assessment of homogeneity of results from different stages are pre-planned

• Undesirable to seek statistical significance at all cost, but larger trial would provide additional information: effect sizes to be discussed in advance

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Seamless Phase II/III Designs

• Goal: address within a single trial objectives that are normally achieved through separate trials in phases IIb and III. In a seamless design, the trial continues through the selection point and into the confirmatory phase for the cohorts that are chosen to continue.

• Adaptive design potentially saves time, uses fewer patients and raise the opportunity to have longer term follow-up data by the end of the confirmatory phase.

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Brannath et al.Target therapy in Oncology

• Goals: – To confirm or disregard a subpopulation S, which is

identified in a separate exploratory study– To confirm the treatment effect of the novel therapy in

the selected target population (sub-population,S, or full population, F)

– Design: Consider 3 stages:• First interim analysis: to decide whether continue recruiting

patients only from S or to continue recruiting from F. • Second Interim: Allows for possible early stopping without

further adaptation (futility or early success)• Final analysis would consist in testing efficacy only in S or in

both , F and S

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Point of estimation: Sub-population • Testing population selection can be handled by

considering the multiple testing problem with the two null hypotheses (H0 {F}, H0 {S} ) of identical survival distributions of treatment and control within the full population F and sub-population S.

• Assessment using Bayesian predictive probabilities

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Point of estimation: treatment effect

• Compute multiplicity adjusted confidence intervals at the level of 50% with simulation studies indicating that the usual partial maximum likelihood estimates will not exhibit a noticeable positive bias.

• Assessment using Bayesian posterior probabilities

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Summary

Pros• Address within a single trial

objectives that are normally achieved through separate trials in phases IIb and III.

• Potentially saves time• Uses fewer patients • Opportunity to have longer

term follow-up data by the end of the confirmatory phase.

Cons• Specifying the trial design \

feature that can be adapted at the interim analysis is recommended and should be defined in the study protocol.

• Provide the IDMC with an adaptation rule before the adaptive interim analysis

• Regulatory buy in: Upfront interaction with regulatory agencies to achieve agreement on expectations and approach

• Ensure trial integrity: Challenge

This approach is discussed in CHMP reflection paper on “Methodological issues in confirmatoryClinical trials planned with an adaptive design” 2007; available at :www.emea.europa.eu/pdfs/human/ewp/245902enadopted.pdf

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Back-ups

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Endpoints

• Adaptive Designs have been proposed as a methodological framework to incorporate changes:– Primary endpoint – Components of a composite primary endpoint – Definition of called responder criteria

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Type I error

• Two possible errors in a statistical decision process:

• Type I: also, α error, or false positive. Reject the null hypothesis when the null hypothesis is true

• Type II: also, β error, or a false negative. Fail to reject the null hypotheses when the null hypothesis is false

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Dose Finding

• Traditional phase I trial: patients enter the trial sequentially often in cohorts usually consisting of 3 patients with all patients in a cohort receiving the same dose. The trial terminated either due to running out of patients or following a specified stopping rule. The MTD is frequently taken to be the recommended dose for a hypothetical further cohort at the trial’s end

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Brannath et al.Target therapy in Oncology

• Challenge in development of targeted therapy: identification of the actual target population

• Traditional steps1. Identify the right sub-population 2. Confirmation of subpopulation sensitivity (Phase II)3. Phase III study in the target population

• With adaptive design we can combine in one study steps 2 and 3.

• Combination of multiple testing methodologies together with Bayesian decision tools. Control of type I error rate independently of the adaptation rule.