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

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.

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.

There is a paucity of data evaluating recent changes in clinical and prescriber characteristics of patients initiating sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1RA). RESEARCH DESIGN AND METHODS U.S.-based administrative claims data (July 2013 to June 2018) were used to identify initiators of SGLT2i and GLP-1RA. RESULTSOver 5 years, empagliflozin initiation (as a proportion of SGLT2i) increased by 57.1% (P < 0.001 for trend), while canagliflozin initiation declined by 75.1% (P < 0.001). Empagliflozin was the only agent within SGLT2i with an increase in the proportion of patients with myocardial infarction, stroke, or heart failure (collectively called CVD-HF) (P < 0.001). Liraglutide initiation (as a proportion of total GLP-1RA) declined by 32.1% (P < 0.001), and dulaglutide initiation increased by 34.1% (P < 0.001); the proportion of patients with CVD-HF increased the most in liraglutide initiators (5.1% increase; P < 0.001). Most prescribers were internists or endocrinologists; cardiologist prescribing remained low (<1%). CONCLUSIONSFor SGLT2i, shifts in preference for empagliflozin followed changes in drug labels and guidelines, while for GLP-1RA, other factors such as price or ease of administration may have led to a preference for dulaglutide over liraglutide.

OBJECTIVE Both sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1RA) demonstrated cardiovascular benefits in randomized controlled trials of patients with type 2 diabetes (T2D) generally &lt;65 years old and mostly with cardiovascular disease. We aimed to evaluate the comparative effectiveness and safety of SGLT2i and GLP-1RA among real-world older adults. RESEARCH DESIGN AND METHODS Using Medicare data (April 2013–December 2016), we identified 90,094 propensity score–matched (1:1) T2D patients ≥66 years old initiating SGLT2i or GLP-1RA. Primary outcomes were major adverse cardiovascular events (MACE) (i.e., myocardial infarction, stroke, or cardiovascular death) and hospitalization for heart failure (HHF). Other outcomes included diabetic ketoacidosis (DKA), genital infections, fractures, lower-limb amputations (LLA), acute kidney injury (AKI), severe urinary tract infections, and overall mortality. We estimated hazard ratios (HRs) and rate differences (RDs) per 1,000 person-years, controlling for 140 baseline covariates. RESULTS Compared with GLP-1RA, SGLT2i initiators had similar MACE risk (HR 0.98 [95% CI 0.87, 1.10]; RD −0.38 [95% CI −2.48, 1.72]) and reduced HHF risk (HR 0.68 [95% CI 0.57, 0.80]; RD −3.23 [95% CI −4.68, −1.77]), over a median follow-up of ∼6 months. They also had 0.7 more DKA events (RD 0.72 [95% CI 0.02, 1.41]), 0.9 more LLA (RD 0.90 [95% CI 0.10, 1.70]), 57.1 more genital infections (RD 57.08 [95% CI 53.45, 60.70]), and 7.1 fewer AKI events (RD −7.05 [95% CI −10.27, −3.83]) per 1,000 person-years. CONCLUSIONS Among older adults, those taking SGLT2i had similar MACE risk, decreased HHF risk, and increased risk of DKA, LLA, and genital infections versus those taking GLP-1RA.

Background: Several glucagon-like peptide agonists (GLP-1RA) and sodium-glucose co-transporter 2 inhibitors (SGLT2i) have demonstrated cardiovascular benefit in type 2 diabetes in large randomized controlled trials in patients with established cardiovascular disease or multiple risk factors. However, few trial participants were on both agents and it remains unknown whether the addition of SGLT2i to GLP-1RA therapy has further cardiovascular benefits. Methods: Patients adding either SGLT2i or sulfonylureas to baseline GLP-1RA were identified within 3 US claims datasets (2013-2018) and were 1:1 propensity score matched (PSM) adjusting for >95 baseline covariates. The primary outcomes were 1) composite cardiovascular endpoint (CCE; comprised of myocardial infarction, stroke, and all-cause mortality) and 2) heart failure hospitalization. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) were estimated in each dataset and pooled via fixed-effects meta-analysis. Results: Among 12,584 propensity-score matched pairs (mean [SD] age 58.3 [10.9] year; male (48.2%)) across the 3 datasets, there were 107 CCE events [incidence rate per 1,000 person-years (IR) = 9.9; 95% CI: 8.1, 11.9] among SGLT2i initiators compared to 129 events [IR = 13.0; 95% CI: 10.9, 15.3] among sulfonylurea initiators corresponding to an adjusted pooled HR of 0.76 (95% CI: 0.59, 0.98); this decrease in CCE was driven by numerical decreases in the risk of MI (HR 0.71, 95% CI: 0.51, 1.003) and all-cause mortality (HR 0.68, 95% CI: 0.40, 1.14) but not stroke (HR 1.05, 95% CI: 0.62, 1.79). For the outcome of heart failure hospitalization, there were 141 events [IR = 13.0; 95% CI: 11.0, 15.2] among SGLT2i initiators versus 206 [IR = 20.8; 95% CI: 18.1, 23.8] events among sulfonylurea initiators corresponding to an adjusted pooled HR of 0.65 (95% CI: 0.50, 0.82). Conclusions: Risk of residual confounding cannot be fully excluded. Individual therapeutic agents within each class may have different magnitudes of effect. In this large real-world cohort of diabetic patients already on GLP-1RA, addition of SGLT2i - compared to addition of sulfonylurea - conferred greater cardiovascular benefit. The magnitude of the cardiovascular risk reduction was comparable to the benefit seen in cardiovascular outcome trials of SGLT2i versus placebo where baseline GLP-1RA use was minimal.

Introduction: High-quality randomised controlled trials (RCTs) provide the most reliable evidence on the comparative efficacy of new medicines. However, non-randomised studies (NRS) are increasingly recognised as a source of insights into the real-world performance of novel therapeutic products, particularly when traditional RCTs are impractical or lack generalisability. This means there is a growing need for synthesising evidence from RCTs and NRS in healthcare decision making, particularly given recent developments such as innovative study designs, digital technologies and linked databases across countries. Crucially, however, no formal framework exists to guide the integration of these data types. Objectives and Methods: To address this gap, we used a mixed methods approach (review of existing guidance, methodological papers, Delphi survey) to develop guidance for researchers and healthcare decision-makers on when and how to best combine evidence from NRS and RCTs to improve transparency and build confidence in the resulting summary effect estimates. Results: Our framework comprises seven steps on guiding the integration and interpretation of evidence from NRS and RCTs and we offer recommendations on the most appropriate statistical approaches based on three main analytical scenarios in healthcare decision making (specifically, ‘high-bar evidence’ when RCTs are the preferred source of evidence, ‘medium,’ and ‘low’ when NRS is the main source of inference). Conclusion: Our framework augments existing guidance on assessing the quality of NRS and their compatibility with RCTs for evidence synthesis, while also highlighting potential challenges in implementing it. This manuscript received endorsement from the International Society for Pharmacoepidemiology.

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