Background: Regulators are evaluating the use of non-interventional real-world evidence (RWE) studies to assess the effectiveness of medical products. The RCT-DUPLICATE initiative uses a structured process to design RWE studies emulating randomized controlled trials (RCTs) and compare results. Here, we report findings of the first 10 trial emulations, evaluating cardiovascular outcomes of antidiabetic or antiplatelet medications. Methods: We selected 3 active-controlled and 7 placebo-controlled RCTs for replication. Using patient-level claims data from US commercial and Medicare payers, we implemented inclusion/exclusion criteria, selected primary endpoints, and comparator populations to emulate those of each corresponding RCT. Within the trial-mimicking populations, we conducted propensity score matching to control for >120 pre-exposure confounders. All study parameters were prospectively defined and protocols registered before hazard ratios (HRs) and 95% confidence intervals (CIs) were computed. Success criteria for the primary analysis were pre-specified for each replication. Results: Despite attempts to emulate RCT design as closely as possible, differences between the RCT and corresponding RWE study populations remained. The regulatory conclusions were equivalent in 6 of 10. The RWE emulations achieved a HR estimate that was within the 95% CI from the corresponding RCT in 8 of 10 studies. In 9 of 10, either the regulatory or estimate agreement success criteria were fulfilled. The largest differences in effect estimates were found for RCTs where second-generation sulfonylureas were used as a proxy for placebo regarding cardiovascular effects. Nine of 10 replications had a standardized difference between effect estimates of <2, which suggests differences within expected random variation. Conclusions: Agreement between RCT and RWE findings varies depending on which agreement metric is used. Interim findings indicate that selection of active comparator therapies with similar indications and use patterns enhances the validity of RWE. Even in the context of active comparators, concordance between RCT and RWE findings is not guaranteed, partially because trials are not emulated exactly. More trial emulations are needed to understand how often and in what contexts RWE findings match RCTs. Clinical Trial Registration: URL: https://clinicaltrials.gov Unique Identifiers: NCT03936049, NCT04215523, NCT04215536, NCT03936010, NCT03936036, NCT03936062, NCT03936023, NCT03648424, NCT04237935, NCT04237922

Background: Healthcare claims databases can provide information on the effects of type 2 diabetes (T2DM) medications as used in routine care, but often do not contain data on important clinical characteristics, which may be captured in electronic health records (EHR). Objectives: To evaluate the extent to which balance in unmeasured patient characteristics was achieved in claims data, by comparing against more detailed information from linked EHR data. Methods: Within a large US commercial insurance database and using a cohort design, we identified T2DM patients initiating linagliptin or a comparator agent within class (i.e., other DPP-4 inhibitors) or outside class (i.e., (pioglitazone or sulfonylureas) between 05/2011-12/2012. We focused on comparators used at a similar stage of diabetes as linagliptin. For each comparison, 1:1 propensity score (PS) matching was used to balance over 100 baseline claims-based characteristics, including proxies of diabetes severity and duration. Additional clinical data from EHRs was available for a subset of patients. We assessed representativeness of the claims-EHR linked subset, evaluated the balance of claims- and EHR-based covariates before and after PS-matching via standardized differences (SD), and quantified the potential bias associated with observed imbalances. Results: From a claims-based study population of 166,613 T2DM patients, 7,219 (4.3%) patients were linked to their EHR data. Claims-based characteristics between the EHR-linked and EHR-unlinked patients were comparable (SD<0.1), confirming representativeness of the EHR-linked subset. The balance of claims-based and EHR-based patient characteristics appeared to be reasonable before PS-matching and generally improved in the PS-matched population, to be SD<0.1 for most patient characteristics and SD<0.2 for select laboratory results and BMI categories, not large enough to cause meaningful confounding. Conclusion: In the context of pharmacoepidemiologic research on diabetes therapy, choosing appropriate comparison groups paired with a new user design and 1:1 PS matching on many proxies of diabetes severity and duration improves balance in covariates typically unmeasured in administrative claims datasets, to an extent that residual confounding is unlikely.

Using real-world data (RWD) from three U.S. claims data sets, we aim to predict the findings of the CARdiovascular Outcome Trial of LINAgliptin Versus Glimepiride in Type 2 Diabetes (CAROLINA) comparing linagliptin versus glimepiride in patients with type 2 diabetes (T2D) at increased cardiovascular risk by using a novel framework that requires passing prespecified validity checks before analyzing the primary outcome. RESEARCH DESIGN AND METHODSWithin Medicare and two commercial claims data sets (May 2011-September 2015), we identified a 1:1 propensity score-matched (PSM) cohort of T2D patients 40-85 years old at increased cardiovascular risk who initiated linagliptin or glimepiride by adapting eligibility criteria from CAROLINA. PSM was used to balance >120 confounders. Validity checks included the evaluation of expected power, covariate balance, and two control outcomes for which we expected a positive association and a null finding. We registered the protocol (NCT03648424, ClinicalTrials.gov) before evaluating the composite cardiovascular outcome based on CAROLINA's primary end point. Hazard ratios (HR) and 95% CIs were estimated in each data source and pooled with a fixed-effects meta-analysis. RESULTSWe identified 24,131 PSM pairs of linagliptin and glimepiride initiators with sufficient power for noninferiority (>98%). Exposure groups achieved excellent covariate balance, including key laboratory results, and expected associations between glimepiride and hypoglycemia ) and between linagliptin and end-stage renal disease (HR 1.08 [0.66-1.79]) were replicated. Linagliptin was associated with a 9% decreased risk in the composite cardiovascular outcome with a CI including the null (HR 0.91 [0.79-1.05]), in line with noninferiority. CONCLUSIONSIn a nonrandomized RWD study, we found that linagliptin has noninferior risk of a composite cardiovascular outcome compared with glimepiride.