Estimating Treatment Effects with a Single Arm Clinical Trial and Data Summaries of an External Control Group

Music City Center 

Randomized controlled trials (RCTs) are the gold standard for assessing new treatments, but they are often infeasible due to ethical or practical challenges. In these cases, single-arm trials, which lack a control arm, are useful, and external control data from previous studies can be leveraged to estimate treatment effects. This paper introduces a method for integrating published data summaries from external control groups into the analysis of single-arm trials. While individual patient-level data is preferable, it is often inaccessible due to privacy and economic constraints. As a result, investigators often have to rely on aggregated summaries, leading to challenges in estimating treatment effects accurately. To overcome these challenges, we propose a method that estimates an interval of potential effects (IPE), offering more reliable and interpretable results than single-point estimates. Our method provides a practical framework for using aggregated external data to inform treatment effect estimates and support decision-making in drug development. To assess the effectiveness of the proposed strategy, we conduct extensive simulation studies and provide theoretical guarantees.

causal Inference

external data

real world data

clinical trials 

Section on Statistics in Epidemiology