An analytic framework for aligning observational and randomized trial data: Application to postmenopausal hormone therapy and coronary heart disease.

Abstract

We describe a conceptual analytic framework for aligning observational and randomized controlled trial (RCT) data. The framework allows one to 1) use observational data to estimate treatment effects comparable to their RCT counterparts, 2) properly include early events that occur soon after treatment initiation in the analysis of observational data, 3) estimate various treatment effects that are of clinical and scientific relevance while appropriately adjusting for time-varying confounders in both the RCT and observational analyses, 4) assess the generalizability of RCT findings in the more diverse populations generally found in the observational data, and 5) combine both types of data to study associations that cannot be addressed by one study or a single dataset. We describe the theoretical application of this framework to the Women's Health Initiative data to examine the relation between postmenopausal hormone therapy and coronary heart disease. The analytic framework can be tailored to specific exposure-outcome associations and data sources, and may be refined as more is learned about its strengths and limitations.