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Optimal Dynamic Regimes: Presenting a Case for Predictive Inference

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  • Arjas Elja

    (University of Helsinki and National Institute for Health and Welfare)

  • Saarela Olli

    (National Institute for Health and Welfare)

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.

Suggested Citation

  • Arjas Elja & Saarela Olli, 2010. "Optimal Dynamic Regimes: Presenting a Case for Predictive Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-21, March.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:10
    DOI: 10.2202/1557-4679.1204
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    References listed on IDEAS

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    1. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    2. Elja Arjas & Jan Parner, 2004. "Causal Reasoning from Longitudinal Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 171-187, June.
    3. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
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