A meta-analytic framework to adjust for bias in external control studies

TL;DR

This paper introduces a meta-analytic framework that adjusts for bias in external control studies using historical data, improving the accuracy of treatment effect estimates in non-randomized settings.

Contribution

The authors develop a novel meta-analytic method to correct bias in external control studies, applicable to time-to-event outcomes, with implementation in an R package.

Findings

The framework successfully corrects bias in simulated data.

Empirical analysis shows adjusted hazard ratios align more closely with true effects.

Method reduces variability and bias in treatment effect estimation.

Abstract

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real-world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single-arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non-randomized studies may not be comparable to control patients -- on either measured or unmeasured variables -- and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta-analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta-analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non-small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta.