Double robust estimation of partially adaptive treatment strategies

TL;DR

This paper develops and compares double robust estimators for partially adaptive treatment strategies, improving precision medicine by allowing for correct model specification in outcome or treatment models, demonstrated through simulations and breast cancer data.

Contribution

It introduces new estimators that are doubly robust for partially adaptive strategies, addressing limitations of existing methods that require modeling all interactions.

Findings

New estimators are unbiased if either the treatment or outcome model is correct.

Existing combined inverse probability weighting methods are biased when the treatment model is misspecified.

The proposed estimators perform well in simulations and real data application.

Abstract

Precision medicine aims to tailor treatment decisions according to patients' characteristics. G-estimation and dynamic weighted ordinary least squares (dWOLS) are double robust statistical methods that can be used to identify optimal adaptive treatment strategies. They require both a model for the outcome and a model for the treatment and are consistent if at least one of these models is correctly specified. It is underappreciated that these methods additionally require modeling all existing treatment-confounder interactions to yield consistent estimators. Identifying partially adaptive treatment strategies that tailor treatments according to only a few covariates, ignoring some interactions, may be preferable in practice. It has been proposed to combine inverse probability weighting and G-estimation to address this issue, but we argue that the resulting estimator is not expected to be double robust. Building on G-estimation and dWOLS, we propose alternative estimators of partially adaptive strategies and demonstrate their double robustness. We investigate and compare the empirical performance of six estimators in a simulation study. As expected, estimators combining inverse probability weighting with either G-estimation or dWOLS are biased when the treatment model is incorrectly specified. The other estimators are unbiased if either the treatment or the outcome model are correctly specified and have similar standard errors. Using data maintained by the Centre des Maladies du Sein, the methods are illustrated to estimate a partially adaptive treatment strategy for tailoring hormonal therapy use in breast cancer patients according to their estrogen receptor status and body mass index. R software implementing our estimators is provided.