Estimation of treatment effects in early phase randomized clinical trials involving multiple data sources for external control.

Abstract

Augmented randomized clinical trials are a valuable design option for early phase clinical trials. The addition of external controls could, on the one hand, increase precision in treatment effect estimates or reduce the number of required control patients for a randomized trial but may, on the other hand, introduce bias. We build on previous work on augmented trials with one external control data source in time-to-event settings and extend it to multiple control data sources. In a comprehensive simulation study, we evaluate existing and novel analysis options mainly based on Bayesian hierarchical models as well as propensity score analysis. Different sources of bias are investigated including population (observable and unobservable confounders), data collection (assessment schedule, real-world vs. clinical trial data), and time trend as well as different types of data like individual patient data (with or without baseline covariates) or summary data. Our simulation study provides recommendations in terms of choice of estimation method as well as choice of data sources. Explicit incorporation of the above-mentioned sources of bias in a simulation study is relevant as the magnitude of deviation from the ideal setting has a significant impact on all investigated estimation methods.