Abstract
ObjectivesElectronic health records (EHR) provide a valuable resource for assessing drug side-effects, but treatments are not randomly allocated in routine care creating the potential for bias. We conduct a case study using the Prior Event Rate Ratio (PERR) Pairwise method to reduce unmeasured confounding bias in side-effect estimates for two second-line therapies for type 2 diabetes, thiazolidinediones, and sulfonylureas. Study Design and SettingsPrimary care data were extracted from the Clinical Practice Research Datalink (n = 41,871). We utilized outcomes from the period when patients took first-line metformin to adjust for unmeasured confounding. Estimates for known side-effects and a negative control outcome were compared with the A Diabetes Outcome Progression Trial (ADOPT) trial (n = 2,545). ResultsWhen on metformin, patients later prescribed thiazolidinediones had greater risks of edema, HR 95% CI 1.38 (1.13, 1.68) and gastrointestinal side-effects (GI) 1.47 (1.28, 1.68), suggesting the presence of unmeasured confounding. Conventional Cox regression overestimated the risk of edema on thiazolidinediones and identified a false association with GI. The PERR Pairwise estimates were consistent with ADOPT: 1.43 (1.10, 1.83) vs. 1.39 (1.04, 1.86), respectively, for edema, and 0.91 (0.79, 1.05) vs. 0.94 (0.80, 1.10) for GI. ConclusionThe PERR Pairwise approach offers potential for enhancing postmarketing surveillance of side-effects from EHRs but requires careful consideration of assumptions.
Highlights
Postmarketing surveillance of new drugs is vital to ensure that patients receive safe and effective treatments
This study demonstrates the application of another promising approach to address unmeasured confounding in nonrandomized studies, the Prior Event Rate Ratio (PERR) method [16e18]
Given the computational advantages of using PERR Pairwise when faced with large sample sizes in electronic health record (EHR) databases, we report Pairwise estimates as our selected within-subject approach with a note that PERR-ALT is an alternative
Summary
Postmarketing surveillance of new drugs (pharmacovigilance) is vital to ensure that patients receive safe and effective treatments. Longitudinal data from electronic health record (EHR) systems, such as the Clinical Practice Research Datalink (CPRD) in the UK, provide an Conflicts of interest: IQVIA funding and authorship declared. Ian Fisher employed by IQVIA contributed to the writing of the report. Data reporting: Only the authors have access to the CPRD data. Code lists will be made available by the authors and deposited in the clinical codes repository www.clinicalcodes.org. University of Exeter Medical School, South Cloisters, St Lukes Campus, Exeter EX1 2LU, UK.
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