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

TL;DR

This paper introduces a standardized, scalable framework for assessing treatment effect heterogeneity in observational healthcare data, enabling more nuanced understanding of how treatment benefits vary across patient risk groups.

Contribution

It extends the risk-based treatment effect assessment approach from RCTs to observational data with a five-step framework and provides an open-source software package for implementation.

Findings

Heterogeneity of ACE inhibitor effects varies across risk strata.

Patients at low risk show negligible benefits, higher risk groups show more pronounced effects.

Residual confounding remains a challenge despite adjustment.

Abstract

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.