Quantitative Issues in Cancer Research Working Seminar

Daniel Schwartz, Ph.D.
Research Fellow, Department of Biostatistics, Harvard T.H. Chan School of Public Health

“Harmonized Estimation of Subgroup Specific Treatment Effects in Randomized Trials: The Use of External Control Data”

ABSTRACT: Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. However, these subgroup analyses are typically complicated by small sample sizes for the subgroups of interest. This can lead to substantial uncertainty on the subgroup-specific treatment effects. In this work we explore the use of external control (EC) data to augment an RCT’s subgroup analysis. We propose “harmonized” estimators of subpopulation-specific treatment effects that leverage EC data. Our approach modifies an initial estimate of the subgroup-specific treatment effects obtained through a user-supplied method (e.g., linear regression) applied to the RCT and EC data. The key idea is to alter these initial subgroup-specific effect estimates to make them coherent with a robust estimate, using only RCT data, of the average treatment effect in the overall enrolled population. In particular, we make the weighted average of the resulting harmonized subgroup-specific estimates match the average effect in the overall population. Through theory and simulation results we show that in important realistic settings the harmonized estimator can both eliminate the bias from using external data and greatly reduce the uncertainty of the subgroup analysis.