Robustifying trial-derived optimal treatment rules for a target population

Ying-Qi Zhao,Catherine M Tangen,Michael L Leblanc,Donglin Zeng

doi:10.1214/19-ejs1540

Abstract

Treatment rules based on individual patient characteristics that are easy to interpret and disseminate are important in clinical practice. Properly planned and conducted randomized clinical trials are used to construct individualized treatment rules. However, it is often a concern that trial participants lack representativeness, so it limits the applicability of the derived rules to a target population. In this work, we use data from a single trial study to propose a two-stage procedure to derive a robust and parsimonious rule to maximize the benefit in the target population. The procedure allows a wide range of possible covariate distributions in the target population, with minimal assumptions on the first two moments of the covariate distribution. The practical utility and favorable performance of the methodology are demonstrated using extensive simulations and a real data application.

Highlights

In the new era of personalized medicine, it has been advocated that treatments should be recommended according to individual patient characteristics to account for considerable heterogeneity among patients’ responses to different treatments (Hayes et al, 2007; Hamburg and Collins, 2010)
Randomized clinical trials (RCTs) are ideal for constructing such rules, since they provide internal validity by ensuring consistency, positivity and no unmeasured confounders (Greenland, 1990; Hernan and Robins, 2006) that may be violated in observational studies
We propose a general criterion assessing the quality of a decision rule for the target population, which includes both the value function and the correct allocation rate to the optimal rule as special cases

Summary

Introduction

In the new era of personalized medicine, it has been advocated that treatments should be recommended according to individual patient characteristics to account for considerable heterogeneity among patients’ responses to different treatments (Hayes et al, 2007; Hamburg and Collins, 2010). Sophisticated statistical methods have been developed to estimate optimal individualized treatment rules using data from randomized trials. Regressionbased methods estimate outcome as a function of patient covariates and treatment, and select the treatment that maximizes the predicted outcome for each individual (Brinkley et al, 2010; Qian and Murphy, 2011; Kang et al, 2014). Some recent developments directly search for the individualized treatment rules that maximize the benefit for future patients (Zhang et al, 2012; Zhao et al, 2012). When the primary outcome of interest is survival time subject to right censoring, some methods have been proposed in this regard using either regression-based methods (Goldberg and Kosorok, 2012; Huang et al, 2014) or direct search methods (Zhao et al, 2015)

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