Causal inference methods for combining randomized trials and observational studies: a review

TL;DR

This review discusses methods for combining randomized trials and observational studies to improve causal inference, addressing issues of generalizability and confounding, with practical comparisons and implementations.

Contribution

It provides a comprehensive overview of recent methods for integrating RCTs and observational data, including new implementations and simulation comparisons.

Findings

Methods improve generalizability of RCTs using observational data

Combining data reduces confounding effects in causal estimates

Simulation and real-world data demonstrate method effectiveness

Abstract

With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from unrepresentativeness, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined RCTs and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of RCTs using the representativeness of observational data. Classical estimators include weighting, difference between conditional outcome models, and doubly robust estimators. We then discuss methods that combine RCTs and observational data to either ensure uncounfoundedness of the observational analysis or to improve (conditional) average treatment effect estimation. We also connect and contrast works developed in both the potential outcomes literature and the structural causal model literature. Finally, we compare the main methods using a simulation study and real world data to analyze the effect of tranexamic acid on the mortality rate in major trauma patients. A review of available codes and new implementations is also provided.