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The Predictive Approaches to Treatment Effect Heterogeneity statement focusedon baseline risk as a robust predictor of treatment effect and providedguidance on risk-based assessment of treatment effect heterogeneity in the RCTsetting. The aim of this study was to extend this approach to the observationalsetting using a standardized scalable framework. The proposed frameworkconsists of five steps: 1) definition of the research aim, i.e., thepopulation, the treatment, the comparator and the outcome(s) of interest; 2)identification of relevant databases; 3) development of a prediction model forthe outcome(s) of interest; 4) estimation of relative and absolute treatmenteffect within strata of predicted risk, after adjusting for observedconfounding; 5) presentation of the results. We demonstrate our framework byevaluating heterogeneity of the effect of angiotensin-converting enzyme (ACE)inhibitors versus beta blockers on three efficacy and six safety outcomesacross three observational databases. The proposed framework can supplement anycomparative effectiveness study. We provide a publicly available R softwarepackage for applying this framework to any database mapped to the ObservationalMedical Outcomes Partnership Common Data Model. In our demonstration, patientsat low risk of acute myocardial infarction received negligible absolutebenefits for all three efficacy outcomes, though they were more pronounced inthe highest risk quarter, especially for hospitalization with heart failure.However, failing diagnostics showed evidence of residual imbalances even afteradjustment for observed confounding. Our framework allows for the evaluation ofdifferential treatment effects across risk strata, which offers the opportunityto consider the benefit-harm trade-off between alternative treatments.

A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases