Subgroup Mixable Inference in Personalized Medicine, with an Application to Time-to-Event Outcomes

TL;DR

This paper introduces a new principle called subgroup mixable estimation for accurately assessing treatment efficacy in mixed subgroups, especially for time-to-event outcomes, addressing limitations of existing measures like Hazard Ratio.

Contribution

It develops a general inference framework for subgroup and mixture efficacy estimation, proposing alternative measures and correcting current practices in personalized medicine.

Findings

Hazard Ratio is unsuitable for mixture populations.

Proposed alternative efficacy measures with valid inference.

Established subgroup mixable estimation principle.

Abstract

Measuring treatment efficacy in mixture of subgroups from a randomized clinical trial is a fundamental problem in personalized medicine development, in deciding whether to treat the entire patient population or to target a subgroup. We show that some commonly used efficacy measures are not suitable for a mixture population. We also show that, while it is important to adjust for imbalance in the data using least squares means (LSmeans) (not marginal means) estimation, the current practice of applying LSmeans to directly estimate the efficacy in a mixture population for any type of outcome is inappropriate. Proposing a new principle called {\em subgroup mixable estimation}, we establish the logical relationship among parameters that represent efficacy and develop a general inference procedure to confidently infer efficacy in subgroups and their mixtures. Using oncology studies with time-to-event outcomes as an example, we show that Hazard Ratio is not suitable for measuring efficacy in a mixture population, and provide alternative efficacy measures with a valid inference procedure.