Quantitative Issues in Cancer Research Working Group Seminar
Riddhiman Saha, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health
Estimating Treatment Effects using Aggregate-level Data from External Controls
Abstract: Randomized controlled trials (RCTs) are the gold standard for assessing new treatments, but they are often infeasible in certain contexts, such as life-threatening or rare diseases, 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, which can lead to challenges in estimating treatment effects accurately. To overcome these challenges, we propose a method that estimates an interval of potential effects (IPE) based on a class of plausible distributions for the control group. This approach offers more reliable and interpretable results than single-point estimates, especially when only limited summary statistics are available. Our method provides a practical framework for using aggregated external data to inform treatment effect estimates and support decision-making in early drug development phases. To assess the effectiveness of the proposed strategy, we conduct extensive simulation studies and provide theoretical guarantees under minimal assumptions.
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