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Reward ignorant modeling of dynamic treatment regimes
Author(s) -
Wallace Michael P.,
Moodie Erica E. M.,
Stephens David A.
Publication year - 2018
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201700322
Subject(s) - personalized medicine , outcome (game theory) , causal inference , inference , computer science , covariate , precision medicine , variety (cybernetics) , identification (biology) , observational study , machine learning , data science , risk analysis (engineering) , data mining , artificial intelligence , econometrics , medicine , bioinformatics , statistics , mathematics , botany , mathematical economics , pathology , biology
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 of simulation studies, and through analysis of data from the International Warfarin Pharmacogenetics Consortium, which also serve as motivation for this work.