Second-line Agents for GlycemicControl for Type 2 Diabetes: AreNewer Agents Better?DOI: 10.2337/dc13-1901
OBJECTIVE
Whilemetformin is generally accepted as thefirst-line agent in treatment of type 2diabetes, there are insufficient evidence and extensive debate about the bestsecond-line agent. We aimed to assess the benefits and harms of four commonlyused antihyperglycemia treatment regimens considering clinical effectiveness,quality of life, and cost.
RESEARCH DESIGN AND METHODS
We developed and validated a new population-based glycemic control Markovmodel that simulates natural variation in HbA1c progression. The model wascalibrated using a U.S. data set of privately insured individuals diagnosed withtype 2 diabetes.We compared treatment intensification ofmetforminmonotherapywith sulfonylurea, dipeptidyl peptidase-4 inhibitor, glucagon-like peptide-1 receptoragonist, or insulin. Outcomemeasures included life-years (LYs), quality-adjusted life-years (QALYs), mean time to insulin dependence, and expected medication cost perQALY fromdiagnosis tofirst diabetes complication (ischemicheart disease,myocardialinfarction, congestive heart failure, stroke, blindness, renal failure, amputation) ordeath.
RESULTS
According to our model, all regimens resulted in similar LYs and QALYs regardlessof glycemic control goal, but the regimen with sulfonylurea incurred significantlylower cost per QALY and resulted in the longest time to insulin dependence. AnHbA1c goal of 7% (53 mmol/mol) produced higher QALYs compared with a goal of8% (64 mmol/mol) for all regimens.
CONCLUSIONS
Use of sulfonylurea as second-line therapy for type 2 diabetes generated glycemiccontrol and QALYs comparable with those associated with other agents but atlower cost. Amodel that incorporates HbA1c and diabetes complications can serveas a useful clinical decision tool for selection of treatment options.
Diabetes is one of the most prevalent and costly chronic medical conditions world-wide, incurring significant burdens on individuals, society, and the health caresystem. It is currently estimated that 25.8 million Americans, or 8.3% of the pop-ulation, have diabetes (1). Glucose-lowering therapies are the cornerstone of di-abetes management, with multiple epidemiological studies linking glycemic controlto a lower risk of diabetes-related complications and mortality. Large randomizedcontrolled trials have demonstrated a reduction in microvascular complications
1Graduate Program in Operations Research,North Carolina State University, Raleigh, NC2Division of Endocrinology, Department of Inter-nal Medicine, Mayo Clinic, Rochester, MN3Department of Public Health Sciences, Univer-sity of Virginia, Charlottesville, VA4Division of Health Care Policy and Research, De-partment of Health Sciences Research, MayoClinic, Rochester, MN5Optum Labs, Cambridge, MA6Department of Industrial and Operations Engi-neering, University of Michigan, Ann Arbor, MI
Received 10 August 2013 and accepted 15January 2014.
This article contains Supplementary Data onlineat http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc13-1901/-/DC1.
Any opinions, findings, and conclusions or recom-mendations expressed in this material are thoseof the authors and do not necessarily reflect theviews of the National Science Foundation.
with intensive glycemic control, e.g.,lowering glycosylated hemoglobin(HbA1c) to ,6.5–8.0% (48d64 mmol/mol), depending on the study (2–9). Ev-idence linking glycemic control to lowermacrovascular disease risk and mortal-ity has been less conclusive; loweringHbA1c among younger patients withnewly diagnosed diabetes did reducecardiovascular event rates andmortalityin the UK Prospective Diabetes Study(UKPDS) (5,6), but further reductionsamong people with long-standing diabe-tes in the Action to Control Cardiovascu-lar Risk in Diabetes (ACCORD) andAction in Diabetes and Vascular Disease:Preterax and Diamicron MR ControlledEvaluation (ADVANCE) studies and Vet-erans Affairs Diabetes Trial (VADT) didnot yield similar results (7–9). The exactglycemic target in the treatment of di-abetes therefore remains controversial,with professional groups and regulatoryorganizations currently recommendinglowering HbA1c to ,6.5% (48 mmol/mol) (10), 7.0% (53 mmol/mol) (11), or8.0% (64 mmol/mol) (12), except inpatients at high risk for hypoglycemiaor those with limited life expectancy ormultiple comorbid conditions that pre-clude safe intensive control.There are currently 11 classes of
approved glucose-lowering medica-tions, and the usage of these medica-tions has varied from 1994 to 2007(13). The 2011 Centers for Disease Con-trol and Prevention diabetes fact sheetreported that 58% of adults with diabe-tes are being treated with oral agent(s),12% with insulin, and 14% with both in-sulin and oral agent(s) (1). Diabetesmedications alone accounted for 11.8%of all prescriptions issued in the U.S. in2012 at a cost of more than 18.3 billionUSD (14). Metformin has a long-standingevidence base for efficacy and safety, isinexpensive, and is regarded by most asthe primary first-line agent in the treat-ment of type 2 diabetes (10,11,15).When metformin fails to achieve ormaintain glycemic goals, another agentshould be added; however, there is noconsensus or sufficient empirical evidencesupporting the use of one second-lineagent over another (16). Over the pastdecade, the mix of secondary agentsused in the treatment of diabetes haschanged significantly, with increasinguse of newer glucose-lowering agentssuch as dipeptidyl peptidase-4 (DPP-4)
inhibitors and glucagon-like peptide-1(GLP-1) receptor agonists in place ofolder and less expensive drugs suchas sulfonylureas. This has resulted in adramatic rise in the cost of diabetesmedications and management; yet, thelong-term clinical benefit of this shift isuncertain (13).
In the absence of clinical trials directlycomparing alternative treatment regi-mens and considering the high costand challenges of running any such tri-als, we developed and validated a newpopulation-based glycemic control modelbased on a Markov chain to compare thereal-world effectiveness and cost of dif-ferent treatment regimens for individualsnewly diagnosed with type 2 diabetes.We used this model to quantify differen-ces among the regimens in terms of life-years (LYs), quality-adjusted life-years(QALYs), and medication cost per QALYnecessary to achieve and maintain glyce-mic control from the time of diagnosis tothe development of first major diabetes-related complication, specifically, ische-mic heart disease, stroke, blindness, renalfailure, amputation, or death from othercause. We specifically chose these micro-and macrovascular complications of dia-betes, as they have been used in mostlarge observational and interventionalstudies of diabetes therapies (5,7–9).Each regimen was tested using therange of currently recommended glyce-mic control goals between HbA1c 6.5%(48 mmol/mol) and 8% (64 mmol/mol)both to confirm model generalizabilityand to identify the potential impact ofdifferent glycemic control goals onpatienthealth, quality of life, and expenditure.
RESEARCH DESIGN AND METHODS
Treatment RegimensWe considered four treatment-inten-sification regimens: metformin, sulfonyl-urea, and insulin (T1); metformin, DPP-4inhibitor, and insulin (T2); metformin,GLP-1 agonist, and insulin (T3); and met-formin and insulin (T4). In each regimen,patients started metformin monotherapywhen HbA1c reached the prespecified gly-cemic control goal. In T1–T3, treatmentwas sequentially intensified by additionof a second-line agent other than insulin,and if or when HbA1c again exceeded theglycemic control goal, insulin was initiated(in place of the second-line agent) as thethird-line agent in combination with met-formin. In T4, treatment was intensified
by directly adding insulin once HbA1c
exceeded the glycemic control goal. Forall regimens, there were no further treat-ment changes once insulin was initiated,as it was assumed to maintain glycemiccontrol.
Markov ModelThe Markov model is based on the 10 dis-crete HbA1c states presented in Supple-mentary Tables 1 and 2. Each state isdefined by the conditional mean HbA1cin a given interval for a patient newlydiagnosed with type 2 diabetes. Themean HbA1c value for each state increaseslinearly with respect to age according to alinear trend factor. This common assump-tion, based on other published glycemiccontrol models (17,18), reflects the ex-pected rise in HbA1c with age and antici-pated deterioration of glycemic control. Atthe beginning of each 3-month period,treatment is initiated/intensified if HbA1cexceeds the glycemic control goal. Treat-ment results in a proportional decrease inHbA1c according to amedication effect es-timated fromobservational data (Table 1).If no diabetes complications or death oc-curs, patients undergo continued HbA1cstate transition based on the 3-month tran-sition probability matrices provided in Sup-plementary Tables 1 and 2. Each treatmentregimen was evaluated using the Markovmodel by backward induction (29). All anal-yses were conducted using MATLABR2012b (MathWorks, Inc., Natick, MA).
Outcome MeasuresWe considered four outcome measuresrelated to primary prevention: expectedLYs, expected QALYs, mean time to insulindependence, and expected medicationcost per QALY for maintaining glycemiccontrol from diagnosis to occurrence offirst diabetes-related complication ordeath. For each period in which no diabe-tes complications or death occurred, LYswere increased by 3 months, QALYs wereadjusted based on the disutility of medi-cations, and the medication cost was cal-culated based on the sum of the costs ofusing medications for 3 months dis-counted at a 3% annual discount rate (30).
Data SourcesA retrospective administrative claims dataset that included medical claims, phar-macy claims, laboratory data, and eligibil-ity information from a large, national U.S.health planwas used to estimate 3-monthHbA1c state transition probabilities
2 Treatment Comparison for Glycemic Control Diabetes Care
(Supplementary Data), to estimate themedication effect on reducing HbA1c(Supplementary Data), and to calibrateand validate our model (SupplementaryData). The individuals covered by thishealth plan are geographically diverseacross the U.S. with greatest representa-tion in the south andmidwest U.S. censusregions. The plan provides fully insuredcoverage for professional (e.g., physi-cian), facility (e.g., hospital), and outpa-tient prescription medication services.Medical (professional, facility) claimsinclude ICD-9, Clinical Modification (ICD-9-CM) diagnosis codes, ICD-9 procedurecodes, Current Procedural Terminology,version 4 procedure codes, HealthcareCommon Procedure Coding System pro-cedure codes, site of service codes, pro-vider specialty codes, and health plan andpatient costs. Outpatient pharmacyclaims provide National Drug Codes fordispensed medications, quantity dis-pensed, drug strength, days’ supply, pro-vider specialty code, and health plan andpatient costs. Laboratory results linked tothe administrative claims data are avail-able for a subset of these patients. Allstudy data were accessed using techni-ques that are in compliance with theHealth Insurance Portability and Account-ability Act of 1996, and no identifiable pro-tected health information was extractedduring the course of the study. Becausethis study involved analysis of preexisting,de-identified data, it was exempt from in-stitutional review board approval.
The population meeting criteria forour study (37,501 individuals) were ageof at least 40 years, diagnosis with type 2diabetes between 1995 and 2010, pre-scription for their first noninsulinglucose-lowering medication at least6 months after enrollment, and at least5 years of continuous enrollment with atleast two HbA1c records and completepharmacy claim data. Type 2 diabeteswas defined using the Healthcare Effec-tiveness Data and Information Set crite-ria (31). Healthcare Effectiveness Dataand Information Set requirements forpharmacy data include at least one anti-hyperglycemia medication prescriptionand, for claim encounter data, the pres-ence of at least one diabetes-specificICD-9 diagnosis codes 250.XX (exclude250.X1 and 250.X3), 357.2X, 362.0X, or366.41 with two annual face-to-faceoutpatient encounters with differentdates of service or one face-to-face in
Table 1—Model parameters for base-case analysis and sensitivity analysis
*Concurrent comorbidities include peripheral vascular disease, atrial fibrillation, ischemicheart disease, congestive heart failure, and blindness. †Patients’ blood pressure, totalcholesterol, and HDL were assumed to be well controlled by antihypertension andantihyperlipidemia medications. ‡The disutility of hypoglycemia associated with insulin is setto be 2.24 times the disutility of hypoglycemia associated with sulfonylurea. This choice ismotivated by the incidence rate of severe hypoglycemia among patients using eachmedication provided in ref. 28. §Weight loss is reflected in terms of gains in quality of life;therefore, it is associated with positive number. ||Values in the range represent the 95% CI ofthe estimated relative effect in reducing HbA1c. Sample sizes for estimating clinical effectwere 2,118 for metformin, 765 for sulfonylurea, 204 for DPP-4 inhibitor, and 477 for GLP-1agonist.
an acute inpatient or emergency depart-ment encounter.
Model Parameters for Base-Case andSensitivity AnalysisModel parameters, including base-casevalues and ranges for sensitivity analysis,are shown in Table 1. We assumed a di-agnosis age of 55.2 years for women and53.6 years for men based on the medianage at time of diagnosis of diabetes in theU.S. as of 2011 (19). The initial HbA1c statedistributions for men and women areshown in Supplementary Tables 1 and 2.Treatment regimens were assumed to befixed for patients living beyond 100 years,and future life expectancy at age 100 yearswas assumed to be 2.24 years for womenand 2.05 years for men based on a 2008U.S. life table (32).The probabilities of diabetes complica-
tions were determined by a patient’s age,sex, ethnicity (Afro-Caribbean or not),smoking status, BMI, HbA1c, systolic bloodpressure, total cholesterol, and HDL cho-lesterol; history of peripheral vascular dis-ease, atrial fibrillation, ischemic heartdisease, and congestive heart failure;and blindness at diagnosis using theUKPDS outcomes model (33). Probabilityof death from other cause was estimatedbased on the Centers for Disease Controland Prevention 2007mortality tables (34).The cost of medications other than in-
sulin was based on the Federal medianprice for generic agents and the averagewholesale price for brand name agentsprovided by theAgency for Healthcare Re-search and Quality Evidence Practice Cen-ters (16). The cost of insulin therapy,including the cost related to self-monitoringof blood glucose, insulin, and insulin-related supplies, was taken from Yeawet al. (23). All costs were inflation ad-justed to 2013 dollars using the consumerprice index method (35). For medicationsother than insulin, the base-case cost wasthe mean price of all brand name andgeneric (if available) medicines, and thecost in the range represents the least andthe most expensive medicines. The base-case cost for insulin was the mean cost ofall insulin regimens including basal insulinregimens, premixed insulin regimens, andbasal-bolus insulin regimens. The cost inthe range represents the average cost forbasal insulin therapy (the least expensiveinsulin therapy) and the average cost forbasal-bolus insulin therapy (the most ex-pensive insulin therapy), respectively.
Medication effect (other than for in-sulin) was estimated based on HbA1c
changes seen with use of these agentsby patients included in the data set andis presented as the relative reduction inHbA1c observed during each 3-monthtreatment interval.
Model Calibration and ValidationTo calibrate and validate the model, weused all available HbA1c pairs at least for3.5 months to ensure at least one 3-month transition, as long as the patientwas not on insulin during that timeperiod. This provided a total of 97,667pairs of HbA1c test results. The lineartrend factor was varied from 0 to 0.25to estimate the trend factor that mini-mized the mean of the sum of thesquared errors (SSE) between the ob-served HbA1c state distribution (deter-mined by the second HbA1c value ineach pair) and the model-generatedHbA1c state distributions. The optimaltrend factor was 0.1075 for men(mean SSE of 0.0022) and 0.105 forwomen (mean SSE of 0.0015). Addi-tional details of the model calibrationand validation can be found in Supple-mentary Data.
RESULTS
Base-Case ResultsTheMarkovmodel–based results showedthat the expected LYs and QALYs fromdiagnosis to first event produced by thefour treatment regimens were similar(Table 2). The maximum differenceamong regimens in the expected LYsto first event, specifically, the differencebetween T4 and T1, was 0.03 years(12.73 days) for women and 0.03 years(11.06 days) formen. Similarly, themax-imum difference among regimens in theexpected QALYs to the first event,
specifically, the difference betweenT4 and T1, was 0.04 QALYs (16.12 qual-ity-adjusted days) for women and 0.04QALYs (14.20 quality-adjusted days) formen. The observed differences in ex-pected LYs and QALYs among regimenswere primarily the result of different ex-pected durations of sustained glycemiccontrol with the three second-lineagents (in combination with metformin).The mean time elapsed between failureof metformin monotherapy and theneed for insulin initiation was 1.05 years(381.99 days) for women and 1.0 year(364.65 days) formenusing T1, 0.62 years(224.50 days) for women and 0.53 years(194.84 days) for men using T2, and0.68 years (247.96 days) for womenand 0.62 years (225.46 days) for menusing T3.
Significant differences were observedin the expected medication cost perQALY incurred by the four treatment reg-imens. Compared with using sulfonylureaas a second-line agent, which was theleast expensive treatment regimen, useof DPP-4 inhibitor (T2) was associatedwith a mean per-person additional med-ication cost of 141 USD per QALY forwomen and 160 USD per QALY formen. Use of GLP-1 agonist (T3) incurreda mean additional medication cost of 191USD per QALY for women and 216 USDper QALY for men compared with T1, anduse of insulin as a second-line agent (T4)incurred a mean additional medicationcost of 150 USD per QALY for womenand 170 USD per QALY formen comparedwith T1.
Sensitivity AnalysesFor any fixed glycemic control goal rang-ing between 6.5% (48 mmol/mol) and8.0% (64 mmol/mol), use of sulfonyl-urea as the second-line agent incurred
Table 2—Base-case comparison of four treatment regimens
Mean time to useinsulin (years) 2.76 2.33 2.40 1.72 2.59 2.13 2.21 1.59
Comparison of the expected LYs, expectedQALYs, expectedmedication cost per QALY, andmeantime from diagnosis to insulin initiation for men and women. Four treatment regimens are T1,metformin plus sulfonylurea plus insulin; T2, metformin plus DPP-4 inhibitor plus insulin; T3,metformin plus GLP-1 agonist plus insulin; and T4, metformin plus insulin.
4 Treatment Comparison for Glycemic Control Diabetes Care
the lowest expected medication costper QALY, and GLP-1 agonist use in-curred the highest expected medicalcost per QALY, among both men andwomen (Fig. 1). Targeting a treatmentgoal of 6.5% (48 mmol/mol) vs. 7% (53mmol/mol) incurred significantly higherexpected medication cost per QALYand a small reduction in the expectedQALYs for all treatment regimens (Fig. 1).All treatment regimens resulted in in-creased expected QALYs and increasedmedication cost per QALY when targetinga treatment goal of 7% (53 mmol/mol)compared with 8% (64 mmol/mol)(Fig. 1).The expected medication cost per
QALY of each of the four treatment regi-mens varied significantly (Fig. 2) as a resultof differential costs incurred by generic(metformin, sulfonylurea) comparedwith brand name (DPP-4, GLP-1) medica-tions and basal insulin compared withbasal plus bolus insulin regimens. T3 ex-hibited the largest variation in the ex-pected medication cost per QALY (503USD per QALY difference for women and453 USD per QALY difference for men),while T2 was associated with the smallestvariation in the expected medication costper QALY (291 USD for women and 261USD for men).When the effects of sulfonylurea, DPP-
4 inhibitor, and GLP-1 agonist on HbA1cwere simultaneously set to be the lower
bound or upper bound of the randomizedcontrol trial (RCT) results on the efficacyof medications (Table 1), the four treat-ment regimens still resulted in similar ex-pected LYs and QALYs from diagnosis tofirst event. The treatment regimen withsulfonylurea as the second-line agent re-sulted in the lowest cost per QALY (2,537USD per QALY for women and 2,612 USDper QALY for men at lower bound and2,388 USD per QALY for women and2,454 USD per QALY for men at upperbound), while the treatment regimenwith GLP-1 agonist as the second-lineagent still produced the highest cost perQALY (2,809 USD per QALY for womenand 2,911 USD per QALY for men at lowerbound and 2,867 USD per QALY forwomen and 2,971 USD per QALY formen at upper bound).
CONCLUSIONS
The conclusions drawn from this studyare based on amodel and thereforemaynot be a perfect representation of whatwould be observed in practice. Directcomparison of four different diabetestreatment regimens by theMarkovmodeldeveloped and validated in this studydemonstrated that all four treatmentregimens resulted in similar expectedbenefits in LYs and QALYs irrespectiveof glycemic control goal. However, for allglycemic control goals ranging betweenthe currently recommended targets of
HbA1c 6.5% (48 mmol/mol) and 8% (64mmol/mol), the use of sulfonylurea asthe second-line agent incurred the lowestexpected medication cost per QALY.These findings hold for both observed ef-fects of medications from real-world dataand randomized control trial results. Thedifferences in cost per patient among thefour treatment regimens were substan-tial and thus of potential importance topatients as well as health care providersand payers. In addition, the treatmentregimen with a sulfonylurea as thesecond-line agent resulted in the longesttime of insulin independence comparedwith all other regimensdan importantfactor to be considered by patients whowish to delay insulin initiation as long aspossible. Conversely, the more expensivetreatment options that use a DPP-4 inhib-itor or a GLP-1 agonist as the second-line agent were associated with slightlyless expected benefit in terms of bothLYs and QALYs, and a shorter time of in-sulin independence, compared with theuse of sulfonylurea. Use of insulin as thesecond-line agent resulted in the shortesttime to insulin dependence, and was alsosignificantly more expensive than usingsulfonylurea with no added benefit interms of LYs or QALYs.
To date, there has been no compre-hensive side-by-side evaluation of theclinical benefits, effects on quality oflife, and costs incurred by different
Figure 1—QALYs versus cost incurred by the four different treatment regimens as a function of glycemic control goal. Comparison of the expectedQALYs versus the expectedmedication cost per QALY incurred from diagnosis to first event (diabetes-related complication or death) for men (A) andwomen (B). Each of the four treatments is compared as the glycemic control goal is varied from 6.5% (48 mmol/mol) to 8% (64 mmol/mol). Resultsare presented using HbA1c of 6.5% (48 mmol/mol) (C), 7% (53 mmol/mol) (▲), and 8% (64 mmol/mol) (-) as the glycemic control goal.
diabetes treatment regimens for glycemiccontrol. Our model fills this gap by inte-grating real-world knowledge of treat-ment costs, benefits, and harm, therebyallowing clinicians, payers, andpatients todirectly compare treatment regimens toselect the one that is best suited for eachindividual patient given his/her specificglycemic control goal, cost sensitivity,and preference. Given that .25 millionpatients have been diagnosed with type2 diabetes in the U.S., the potential policyimplications of these differences uncov-ered by our model are also significant.The Glycemia Reduction Approaches in
Diabetes: A Comparative EffectivenessStudy (GRADE), which is in the recruit-ment phase now, seeks to compare thesame treatment regimens using a pro-spective clinical trial design; however,our model is significantly different fromthat of GRADE in that our resultscompare QALYs and costs for newlydiagnosed patients and because ourtreatment efficacy is based on data thatcaptures long-term adherence effectsthat are typically much smaller in clinicaltrials.Severalmodels havebeendeveloped to
predict the natural history of diabetes-
related complications progression and togauge their sequelae on patient quality oflife (17,18,36–38); however, none of thesemodels were based on real-world data de-scribing the rate of and variations in HbA1cprogression caused by both biologicalchanges and patient behavior with andwithout different treatment modalities.Moreover, none of the previous publishedmodels explicitly compared and con-trasted different treatment regimenswith regard to their practical efficiency,cost, and clinical benefit based on real-world inputs rather than clinical trialdata or select observational study popula-tion groups. To our knowledge, this studyis the first to develop and validate a gly-cemic control model that takes into con-sideration the known adverse effects oftreatment, such as hypoglycemia, currentmedication cost, and various suggestedglycemic control goals.
Our model can serve as an adjunctivedecision aid to facilitate treatment se-lection for people newly diagnosedwith type 2 diabetes in a way that tradesoff health and economic implications forpatients. It can also be used by healthcare providers and payers to determinewhether a particular treatment option is
consistent with the goal of high-valuecare, e.g., providing a clinically justifiedbenefit given the incurred cost. Whileno clinical study has yet definitively es-tablished the clinical benefit of using in-cretins in place of sulfonylureas assecond-line agents and there is increas-ing concern regarding sulfonylurea useowing to its association with severehypoglycemia (10), our model, whichconsiders the side effect of severe hypo-glycemia, suggests that for a glycemiccontrol goal of 6.5% (48 mmol/mol) or7% (53 mmol/mol), sulfonylureas pro-vide higher value than incretin. Indeed,use of incretins as second-line agents(treatment regimens T2 and T3) resultedin significantly higher cost but slightlyless clinical benefit as measured by LYsand QALYs to first incident diabetes-related complication or death. However,ultimate value will depend on patientpreference.
Our study has several limitations.First, the results presented in this man-uscript are based on a Markov modelrather than a clinical trial, and no modelcan provide a perfect representation ofreality. Specifically, our model assumesthat HbA1c varies among discrete states
Figure 2—Sensitivity analysis on the medication cost. The x-axis represents the difference in the expected medication cost per QALY from the base-case cost: metformin costs 81.75 USD per month, sulfonylurea costs 54.85 USD per month, DPP-4 inhibitor costs 232.84 USD per month, GLP-1agonist costs 325.97 USD per month, and insulin therapy costs 245.70 USD per month. The y-axis represents the treatment regimen. The solid barrepresents men, and the hatched bar represents women. met, metformin; sulf, sulfonylurea.
6 Treatment Comparison for Glycemic Control Diabetes Care
and at discrete 3-month time intervalsrather than continuously; furthermore,transitions among states are assumed todepend only on the most recent HbA1cstate. For addressing these limitations,the assumptions were carefully vali-dated based on real patient data. Treat-ment regimens were designed assequential one-by-one additions of dif-ferent classes of antihyperglycemicmedications, while in clinical practice pa-tientsmay start two ormore drugs at thesame time.We also assumed that insulinwould replace the previously used sec-ond-line drug, as recommended by mostclinical practice guidelines, but it is pos-sible for patients to continue using twoormore noninsulin agents in conjunctionwith insulin. We assumed that insulinwill ultimately result in achievement ofthe glycemic goal; this is an idealizedassumption that is based on the physiol-ogy of insulin action, and there is likely tobe substantial variation among patientsin whether they achieve and maintaintheir glycemic goal over time. Finally, themodel is based on data that representsa privately insured population. Therefore,it is possible that these results may notbe generalizable to the Medicare andMedicaid populations.Several features that were not incorpo-
rated into the current model are due toinsufficient evidence in literature such asthe potential variability in how medica-tions influence HbA1c trajectory, the po-tential variability in the duration ofobserving the effect of medications, andthe potential indirect pleiotropic effectsof these medications not mediated bytheir glucose-lowering properties.Medica-tion disutility valueswere based on limitedempirical data because definitive evidenceis not yet available. Our analyses werefocused on primary prevention of themost common micro- and macrovascularcomplications of diabetes, and patients in-cluded were treatment naıve and newlydiagnosed with diabetes. To the extentpossible, we have used previously pub-lished data on the utility decrements forcomplications and treatments; however,utility estimates are limited in that theyrepresent an average measure and donot reflect individual patients’ well-being.To address this, we performed sensitivityanalysis on the utility estimates. Finally,not all known adverse medication ef-fects were included in the model. Wedid not consider severe nausea and other
gastrointestinal side effects of metforminor DPP-4 inhibitors (16), since these symp-toms andavailability of alternativeswouldlikely cause the medication to be discon-tinued. We did not consider pancreatitisrisk from the new agents due to the un-certainty of this evidence (39,40). Ulti-mately, however, our proposed model issufficiently versatile to allow for easy in-tegration of newly acquired clinical knowl-edge and its continued refinement.
Two key factors that were not explicitlyincorporated into the model are medi-cation adherence and lifestyle modi-fications, both of which are known toimprove glycemic control, particularly inearly stages of diabetes. However, this isalleviated by our use of real-world obser-vational data for patients who adhere totheir treatments and lifestyle recommen-dations with the frequency expectedfrom any general population amongwhich such therapies are to be deployed.This affords our model an aspect of gen-eralizability and validity that makes it at-tractive and relevant to patients, healthcare providers, and payers.
Funding. This work was funded in part by Agencyfor Healthcare Research and Quality under grant1R21-HS-017628 (to N.D.S.). This material is alsobased in part on work supported by the NationalScience Foundation under grant no. CMMI-0969885 (to B.T.D.).
The funders had no role in the study design,data collection and analysis, decision to publish,or preparation of the manuscript.Duality of Interest. No potential conflicts ofinterest relevant to this article were reported.Author Contributions. Y.Z. designed the study,developed themodel, performed the data analysis,interpreted the results, wrote the manuscript, andreviewed and edited the manuscript. R.G.M.contributed to the results interpretation anddiscussion, wrote the manuscript, and reviewedand edited the manuscript. J.E.M. provided criticalrecommendations regarding study design andmethodology, contributed to the results interpre-tation and discussion, and reviewed and edited themanuscript. S.A.S. provided critical recommenda-tions regarding study design, contributed to theresults interpretation and discussion, and reviewedandedited themanuscript.N.D.S. provided fundingfor the acquisition of data, provided critical recom-mendations regarding study design, contributed tothe results interpretation and discussion, andreviewed and edited the manuscript. B.T.D. pro-vided funding for the study, provided criticalrecommendations regarding study design andmethodology, contributed to the results interpre-tation and discussion, and reviewed and edited themanuscript. Y.Z. is the guarantor of this work and,as such, had full access to all the data in the studyand takes responsibility for the integrity of the dataand the accuracy of the data analysis.
References1. Centers for Disease Control and Prevention.National Diabetes Fact Sheet: National Estimatesand General Information on Diabetes and Predia-betes in the United States, 2011. Atlanta, GA, De-partment of Health and Human Services, Centersfor Disease Control and Prevention, 20112. The Diabetes Control and Complications TrialResearch Group. The effect of intensive treat-ment of diabetes on the development and pro-gression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med1993;329:977–9863. Nathan DM, Cleary PA, Backlund JY, et al.;Diabetes Control and Complications Trial/Epi-demiology of Diabetes Interventions and Com-plications (DCCT/EDIC) Study Research Group.Intensive diabetes treatment and cardiovascu-lar disease in patients with type 1 diabetes. NEngl J Med 2005;353:2643–26534. Ohkubo Y, Kishikawa H, Araki E, et al. Inten-sive insulin therapy prevents the progression ofdiabetic microvascular complications in Japa-nese patients with non-insulin-dependent dia-betesmellitus: a randomized prospective 6-yearstudy. Diabetes Res Clin Pract 1995;28:103–1175. UK Prospective Diabetes Study (UKPDS)Group. Intensive blood-glucose control with sul-phonylureas or insulin compared with conven-tional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33).Lancet 1998;352:837–8536. Stratton IM, Adler AI, Neil HA, et al. Associa-tion of glycaemia with macrovascular and mi-crovascular complications of type 2 diabetes(UKPDS 35): prospective observational study.BMJ 2000;321:405–4127. Ismail-Beigi F, Craven T, Banerji MA, et al.;ACCORD trial group. Effect of intensive treatmentof hyperglycaemia on microvascular outcomes intype 2 diabetes: an analysis of the ACCORD rand-omised trial. Lancet 2010;376:419–4308. Patel A, MacMahon S, Chalmers J, et al.; AD-VANCE Collaborative Group. Intensive bloodglucose control and vascular outcomes inpatients with type 2 diabetes. N Engl J Med2008;358:2560–25729. Duckworth W, Abraira C, Moritz T, et al.;VADT Investigators. Glucose control and vascu-lar complications in veterans with type 2 diabe-tes. N Engl J Med 2009;360:129–13910. Garber AJ, Abrahamson MJ, Barzilay JI,et al.; American Association of Clinical Endocri-nologists. AACE comprehensive diabetes man-agement algorithm 2013. Endocr Pract 2013;19:327–33611. American Diabetes Association. Standardsof medical care in diabetesd2013. DiabetesCare 2013;36(Suppl. 1):S11–S6612. Riethof M, Flavin PL, Lindvall B, et al.; In-stitute for Clinical Systems Improvement. Diag-nosis and management of type 2 diabetesmellitus in adults [internet], 2012. Availablefrom http://bit.ly/diabetes0412. Accessed27 May 201313. Alexander GC, Sehgal NL, Moloney RM,Stafford RS. National trends in treatment oftype 2 diabetesmellitus, 1994-2007. Arch InternMed 2008;168:2088–209414. American Diabetes Association. Economiccosts of diabetes in the U.S. in 2012. DiabetesCare 2013;36:1033–1046
15. Holman RR, Paul SK, Bethel MA, MatthewsDR, Neil HA. 10-year follow-up of intensive glu-cose control in type 2 diabetes. N Engl J Med2008;359:1577–158916. Bennett WL, Wilson LM, Bolen S, et al. Oraldiabetes medications for adults with type 2 di-abetes: an update. In Comparative EffectivenessReviews, No. 27. Rockville, MD, U.S. Agency forHealthcare Research and Quality, 201117. CDC Diabetes Cost-effectiveness Group.Cost-effectiveness of intensive glycemic con-trol, intensified hypertension control, and se-rum cholesterol level reduction for type 2diabetes. JAMA 2002;287:2542–255118. Chen J, Alemao E, Yin D, Cook J. Develop-ment of a diabetes treatment simulationmodel:with application to assessing alternative treat-ment intensification strategies on survival anddiabetes-related complications. Diabetes ObesMetab 2008;10(Suppl. 1):33–4219. Centers for Disease Control and Prevention.Age at diagnosis of diabetes among adult incidentcases aged 18-79 years [internet]. Available fromhttp://www.cdc.gov/diabetes/statistics/incidence_national.htm. Accessed 28 May 201320. Kramer H, Cao G, Dugas L, Luke A, Cooper R,Durazo-Arvizu R. IncreasingBMI andwaist circum-ference and prevalence of obesity among adultswith Type 2 diabetes: the National Health andNutrition Examination Surveys. J Diabetes Compli-cations 2010;24:368–37421. Expert Panel on Detection, Evaluation, andTreatment of High Blood Cholesterol in Adults.Executive Summary of the Third Report of TheNational Cholesterol Education Program(NCEP) Expert Panel on Detection, Evaluation,and Treatment of High Blood Cholesterol inAdults (Adult Treatment Panel III). JAMA2001;285:2486–249722. Sinha A, Rajan M, Hoerger T, Pogach L.Costs and consequences associated with newermedications for glycemic control in type 2 di-abetes. Diabetes Care 2010;33:695–70023. Yeaw J, Lee WC, Aagren M, Christensen T.Cost of self-monitoring of blood glucose in the
United States among patients on an insulinregimen for diabetes. J Manag Care Pharm2012;18:21–3224. DeFronzo RA, Stonehouse AH, Han J, WintleME. Relationship of baseline HbA1c and efficacyof current glucose-lowering therapies: a meta-analysis of randomized clinical trials. DiabetMed 2010;27:309–31725. Hermann LS, Schersten B, Bitzen PO,Kjellstrom T, Lindgarde F, Melander A. Thera-peutic comparison of metformin and sulfonyl-urea, alone and in various combinations. Adouble-blind controlled study. Diabetes Care1994;17:1100–110926. Russell-Jones D, Cuddihy RM, Hanefeld M,et al.; DURATION-4 Study Group. Efficacy andsafety of exenatide once weekly versus metfor-min, pioglitazone, and sitagliptin used as mono-therapy in drug-naive patients with type 2diabetes (DURATION-4): a 26-week double-blind study. Diabetes Care 2012;35:252–25827. Moretto TJ, Milton DR, Ridge TD, et al. Effi-cacy and tolerability of exenatide monotherapyover 24 weeks in antidiabetic drug-naivepatients with type 2 diabetes: a randomized,double-blind, placebo-controlled, parallel-group study. Clin Ther 2008;30:1448–146028. Shorr RI, Ray WA, Daugherty JR, Griffin MR.Incidence and risk factors for serious hypogly-cemia in older persons using insulin or sulfony-lureas. Arch Intern Med 1997;157:1681–168629. Puterman ML. Markov Decision Processes:Discrete Stochastic Dynamic Programming.Hoboken, NJ, John Wiley & Sons, Inc., 199430. Siegel JE, Weinstein MC, Russell LB, GoldMR; Panel on Cost-Effectiveness in Health andMedicine. Recommendations for reportingcost-effectiveness analyses. JAMA 1996;276:1339–134131. National Committee for Quality Assurance(NCQA). HEDIS 2009 Volume 2: Technical Up-date [Internet]. Available from http://www.ncqa.org/portals/0/PolicyUpdates/HEDIS%20Technical%20Updates/2009_Vol2_Technical_Update.pdf. Accessed 20 May 2013
32. Arias E. United States life tables, 2008. InNational Vital Statistics Reports. Hyattsville,MD, National Center for Health Statistics, 201233. Clarke PM, Gray AM, Briggs A, et al.; UKProspective Diabetes Study (UKDPS) Group. Amodel to estimate the lifetime health outcomesof patients with type 2 diabetes: the UnitedKingdom Prospective Diabetes Study (UKPDS)Outcomes Model (UKPDS no. 68). Diabetologia2004;47:1747–175934. Centers for Disease Control and Prevention.Deaths, percent of total deaths, and death ratesfor the 15 leading causes of death in 10-year agegroups, by race and sex: United States, 1999–2010 [internet]. Available from http://www.cdc.gov/nchs/nvss/mortality/lcwk2.htm.Accessed 20 May 201335. U.S. Bureau of Labor Statistics. CPI inflationcalculator [internet]. Available from http://www.bls.gov/data/inflation_calculator.htm.Accessed 20 May 201336. Bagust A, Hopkinson PK,MaierW, Currie CJ.An economic model of the long-term healthcare burden of Type II diabetes. Diabetologia2001;44:2140–215537. Eastman RC, Javitt JC, Herman WH, et al.Model of complications of NIDDM. I.Model con-struction and assumptions. Diabetes Care 1997;20:725–73438. Zhou H, Isaman DJ, Messinger S, et al. Acomputer simulation model of diabetes pro-gression, quality of life, and cost. DiabetesCare 2005;28:2856–286339. Singh S, Chang HY, Richards TM, Weiner JP,Clark JM, Segal JB. Glucagonlike peptide1-based therapies and risk of hospitalizationfor acute pancreatitis in type 2 diabetes melli-tus: a population-based matched case-controlstudy. JAMA Intern Med 2013;173:534–53940. Dore DD, Seeger JD, Arnold Chan K. Use of aclaims-based active drug safety surveillance sys-tem to assess the risk of acute pancreatitis withexenatide or sitagliptin compared to metforminor glyburide. Curr Med Res Opin 2009;25:1019–1027
8 Treatment Comparison for Glycemic Control Diabetes Care
Appendix A. HbA1c Transition Probability Matrix Estimation To estimate the 3-month HbA1c transition probabilities, we selected all pairs of HbA1c records from the 37,501 eligible patients such that the period between tests was between 2.5 and 3.5 months and the patient was not on insulin during that time period. This resulted in 30,249 pairs (multiple pairs permitted per patient). Using the observed HbA1c value, , of patient at time epoch , the corresponding natural HbA1c value (without
medication), , was estimated as:
Where denotes patient i’s current treatment regimen and is the estimated relative reduction in HbA1c
when patient is using treatment regimen at time period (Table 1). We discretized all natural HbA1c values
into 10 HbA1c states as defined in Supplement Table s1 and Supplement Table s2. For any two HbA1c states, and , we denoted the total number of transitions from state to state as . The maximum likelihood estimate
of the transition probability from state to state was estimated as:
where is the set of HbA1c states. Appendix B. Treatment Effect Estimation Five classes of glucose-lowering medications were considered: metformin, sulfonylurea, DPP-IV inhibitor, GLP-1 agonist, and insulin. We assumed that once insulin was initiated, HbA1c would be maintained at a predefined level (it is set to be in our numerical experiments). We also assumed that medications other than insulin had additive effect in reducing HbA1c (1); therefore, each medication effect was estimated independently.
For each medication other than insulin, we selected patients who had at least one HbA1c record within 3 months before and after its initiation, and who were treated with this medication for at least 3 consecutive months. For each selected patient, we calculated the pre-treatment HbA1c and the post-treatment HbA1c by taking the mean of his/her HbA1c records during the 3-month intervals before and after the date of initiation, respectively. The medication effect shown in Table 1 was then calculated as the overall mean relative change between the pre-treatment HbA1c and the post-treatment HbA1c of all the selected patients.
Appendix C. Model Calibration and Validation
To calibrate and validate the model we used all HbA1c pairs of the eligible 37,501 patients such that the period between HbA1c tests was greater than or equal to 3.5 months (in order to have at least one 3-month transition) and the patient was not on insulin during that time period. This resulted in 97,667 pairs.
For each value of the linear trend factor, between and , and for each initial test result in each
pair, we simulated the second test result in the pair 100 times using the 3-month HbA1c transition probability matrix (Supplement Table s1 and Supplement Table s2) and the number of transitions, , determined by the
time interval between the two HbA1c tests of each pair . Using the model-generated natural HbA1c state, ,
for each pair , we calculated the model-generated HbA1c value, with medications initiated during
the time interval as follows:
,
where is the mean natural HbA1c value of being in the HbA1c state at diagnosis
(Supplement Table s1 and Supplement Table s2) and is the medication effect of using medications
. Finally, we determined the model-generated HbA1c state for that pair based on the model-generated HbA1c
value. Given the 100 model-generated 97,667 HbA1c pairs, we calculated the mean of the sum of the squared
errors (SSE) between the model-generated HbA1c state distribution and the observed HbA1c state distribution as:
where represents the observed HbA1c state probability distribution (based on the
second HbA1c values in all pairs) and the vector represents the model-generated HbA1c state probability
distribution for the simulation with a fixed linear trend value . The best linear trend was selected as the one
that minimizes the mean of the SSE. We found that the optimal trend factor was 0.1075 for men (mean SSE of 0.0022) and 0.105 for women (mean SSE of 0.0015) with the median difference between the observed HbA1c distribution and the simulated HbA1c distribution of 0.0096 (minimum: 0.0027, maximum 0.0271) for men and 0.0055 (minimum: 0.0000, maximum: 0.0197) for women.
Supplementary Table 1. Glycosylated hemoglobin (HbA1c) used in the Markov model for women. HbA1c range definition at diagnosis, the mean natural HbA1c values for each HbA1c state at diagnosis (prior to initiating medication), the initial HbA1c distributions at diagnosis, and 3-month HbA1c transition probability matrices for men and women.
Supplementary Table 2. Glycosylated hemoglobin (HbA1c) used in the Markov model for men. HbA1c range definition at diagnosis, the mean natural HbA1c values for each HbA1c state at diagnosis (prior to initiating medication), the initial HbA1c distributions at diagnosis, and 3-month HbA1c transition probability matrices for men and women.
1. Bennett WL, Wilson LM, Bolen S, et al. Oral Diabetes Medications for Adults With Type 2 Diabetes: An Update. In: Comparative Effectiveness Reviews. No. 27. Rockville, MD: Agency for Healthcare Research and Quality (US); 2011
