  The Predictive Approaches to Treatment Effect Heterogeneity statement focused
on baseline risk as a robust predictor of treatment effect and provided
guidance on risk-based assessment of treatment effect heterogeneity in the RCT
setting. The aim of this study was to extend this approach to the observational
setting using a standardized scalable framework. The proposed framework
consists of five steps: 1) definition of the research aim, i.e., the
population, the treatment, the comparator and the outcome(s) of interest; 2)
identification of relevant databases; 3) development of a prediction model for
the outcome(s) of interest; 4) estimation of relative and absolute treatment
effect within strata of predicted risk, after adjusting for observed
confounding; 5) presentation of the results. We demonstrate our framework by
evaluating heterogeneity of the effect of angiotensin-converting enzyme (ACE)
inhibitors versus beta blockers on three efficacy and six safety outcomes
across three observational databases. The proposed framework can supplement any
comparative effectiveness study. We provide a publicly available R software
package for applying this framework to any database mapped to the Observational
Medical Outcomes Partnership Common Data Model. In our demonstration, patients
at low risk of acute myocardial infarction received negligible absolute
benefits for all three efficacy outcomes, though they were more pronounced in
the highest risk quarter, especially for hospitalization with heart failure.
However, failing diagnostics showed evidence of residual imbalances even after
adjustment for observed confounding. Our framework allows for the evaluation of
differential treatment effects across risk strata, which offers the opportunity
to consider the benefit-harm trade-off between alternative treatments.
