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There is tremendous interest in precision medicine as a means to improve patient out- comes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often seek to balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite out- comes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the pro- posed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression.
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Optimal Treatment Regimes: A Review and Empirical Comparison
A treatment regime is a sequence of decision rules, one per decision point, that maps accumulated patient information to a recommended intervention. An optimal treatment regime maximises expected cumulative utility if applied to select interventions in a population of interest. As a treatment regime seeks to improve the quality of healthcare by individualising treatment, it can be viewed as an approach to formalising precision medicine. Increased interest and investment in precision medicine has led to a surge of methodological research focusing on estimation and evaluation of optimal treatment regimes from observational and/or randomised studies. These methods are becoming commonplace in biomedical research, although guidance about how to choose among existing methods in practice has been somewhat limited. The purpose of this review is to describe some of the most commonly used methods for estimation of an optimal treatment regime, and to compare these estimators in a series of simulation experiments and applications to real data. The results of these simulations along with the theoretical/methodological properties of these estimators are used to form recommendations for applied researchers.
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- PAR ID:
- 10467921
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- International Statistical Review
- ISSN:
- 0306-7734
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/insr.12536
