Reward ignorant modeling of dynamic treatment regimes

Biometrical Journal. Biometrische Zeitschrift
Michael P WallaceDavid A Stephens

Abstract

Personalized medicine optimizes patient outcome by tailoring treatments to patient-level characteristics. This approach is formalized by dynamic treatment regimes (DTRs): decision rules that take patient information as input and output recommended treatment decisions. The DTR literature has seen the development of increasingly sophisticated causal inference techniques that attempt to address the limitations of our typically observational datasets. Often overlooked, however, is that in practice most patients may be expected to receive optimal or near-optimal treatment, and so the outcome used as part of a typical DTR analysis may provide limited information. In light of this, we propose considering a more standard analysis: ignore the outcome and elicit an optimal DTR by modeling the observed treatment as a function of relevant covariates. This offers a far simpler analysis and, in some settings, improved optimal treatment identification. To distinguish this approach from more traditional DTR analyses, we term it reward ignorant modeling, and also introduce the concept of multimethod analysis, whereby different analysis methods are used in settings with multiple treatment decisions. We demonstrate this concept through a variety ...Continue Reading

References

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Sep 26, 2017·Journal of the American Statistical Association·Xin ZhouMichael R Kosorok

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Citations

May 11, 2019·Annual Review of Statistics and Its Application·Michael R Kosorok, Eric B Laber
Feb 24, 2021·BMC Medical Research Methodology·Robert K MaharJulie A Simpson
Jan 29, 2022·Statistics in Medicine·Coraline Danieli, Erica E M Moodie

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