Estimating the Treatment Effect in a Subgroup Defined by an Early Post-Baseline Biomarker Measurement in Randomized Clinical Trials With Time-To-Event Endpoint

Abstract

Abstract–Biomarker measurements can be relatively easy and quick to obtain and are useful to investigate whether a compound works as intended on a mechanistic, pharmacological level. In some situations, it is realistic to assume that patients, whose post-baseline biomarker levels indicate that they do not sufficiently respond to the drug, are also unlikely to respond on clinically relevant long-term outcomes (such as time-to-event). However, the determination of the treatment effect in the subgroup of patients that would be biomarker responders, if given treatment, is not straightforward in a parallel groups trial: it is unclear which patients on placebo would have responded had they been given the treatment, so that naive comparisons between treatment and placebo will not estimate the treatment effect of interest. The purpose of this article is to investigate assumptions necessary to obtain causal conclusions in such a setting, using the formalism and existing strategies from causal inference. Three approaches for estimation of subgroup effects will be developed and illustrated using simulations and a case-study.

Keywords:

• Causal inference
• Estimand
• Principal stratification
• Subgroup analysis
• Weighting

Acknowledgments

The authors would like to thank Daniel Scharfstein for sharing ideas and discussions on the topic, as well as feedback on early versions of the methods described in this article. We would also like to thank Heinz Schmidli, Simon Wandel, and Nathalie Ezzet as well as three anonymous reviewers who provided valuable comments on the methods and earlier versions of the manuscript.