Optimal dynamic regimes: presenting a case for predictive inference.

The International Journal of Biostatistics
Elja Arjas, Olli Saarela

Abstract

Dynamic treatment regime is a decision rule in which the choice of the treatment of an individual at any given time can depend on the known past history of that individual, including baseline covariates, earlier treatments, and their measured responses. In this paper we argue that finding an optimal regime can, at least in moderately simple cases, be accomplished by a straightforward application of nonparametric Bayesian modeling and predictive inference. As an illustration we consider an inference problem in a subset of the Multicenter AIDS Cohort Study (MACS) data set, studying the effect of AZT initiation on future CD4-cell counts during a 12-month follow-up.

References

Mar 14, 2002·Biometrics·Constantine E Frangakis, Donald B Rubin
Aug 11, 2007·Biometrics·Erica E M MoodieDavid A Stephens
Aug 30, 2008·Statistics in Medicine·J Kyle Wathen, Peter F Thall

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Citations

Dec 9, 2014·Statistics in Biosciences·Jessica K BarrettSusanne Rosthøj
Aug 12, 2015·Biometrical Journal. Biometrische Zeitschrift·Olli SaarelaErica E M Moodie
Oct 23, 2012·Journal of the Royal Statistical Society. Series C, Applied Statistics·Susan M Shortreed, Erica E M Moodie
Jan 29, 2013·The Canadian Journal of Statistics = Revue Canadienne De Statistique·Erica E M MoodieMichael S Kramer
Jan 17, 2019·American Journal of Epidemiology·Ryan P KyleMichał Abrahamowicz
Jan 5, 2018·Statistical Methods in Medical Research·Alexander P KeilJessie K Edwards
Feb 12, 2019·Journal of the American Statistical Association·Thomas A MurrayPeter F Thall
Oct 6, 2020·Journal of the American Statistical Association·Qian GuanDipankar Bandyopadhyay
Feb 24, 2021·BMC Medical Research Methodology·Robert K MaharJulie A Simpson

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