Speaker
Description
Binary endpoints are commonly used to measure clinical outcomes in randomized controlled trials. In this context, conditional odds ratios (ORs) based on logistic regression have been routinely used as population-level summary to quantify treatment effects. However, ORs have been criticized for a lack of interpretability, non-collapsibility, and sensitivity to model specification. In response, risk differences (RDs) have gained traction as a more interpretable and clinically relevant measure that better aligns with typical estimands of interest in clinical trials. However, assessing covariate-adjusted RDs, especially in small-sample settings (N ≤ 150) typical of early-phase trials, remains methodologically challenging. Motivated by recent regulatory guidance and ongoing methodological discussions on covariate adjustment for unconditional estimators, we systematically evaluate a broad set of statistical methods for assessing RDs in a large-scale simulation study, including various g-computation approaches, Mantel-Haenszel methods, and unconditional tests. Our findings reveal that some g-computation variants with parametric variance estimators fail to maintain nominal Type I error rates in small samples. In contrast, bootstrap-based and Mantel-Haenszel methods may offer a more favorable balance between error control and statistical power. Based on our results, we provide practical recommendations to guide practitioners in selecting statistical methods that (1) target a desired estimand, (2) perform reliably under small-sample conditions, and (3) balance robustness, efficiency, and interpretability. Thereby, we hope to support more reliable and clinically meaningful inference from small-sample clinical trials.
53573503879