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Optimal Structural Nested Models for Optimal Sequential Decisions

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Proceedings of the Second Seattle Symposium in Biostatistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 179))

Abstract

I describe two new methods for estimating the optimal treatment regime (equivalently, protocol, plan or strategy) from very high dimesional observational and experimental data: (i) g-estimation of an optimal double-regime structural nested mean model (drSNMM) and (ii) g-estimation of a standard single regime SNMM combined with sequential dynamic-programming (DP) regression. These methods are compared to certain regression methods found in the sequential decision and reinforcement learning literatures and to the regret modelling methods of Murphy (2003). I consider both Bayesian and frequentist inference. In particular, I propose a novel “Bayes-frequentist compromise” that combines honest subjective non- or semiparametric Bayesian inference with good frequentist behavior, even in cases where the model is so large and the likelihood function so complex that standard (uncompromised) Bayes procedures have poor frequentist performance.

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Author information

Authors and Affiliations

  1. Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

    James M. Robins

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  1. James M. Robins
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Editors and Affiliations

  1. School of Public Health, Department of Biostatistics, CB #7420, University of North Carolina, Chapel Hill, NC, 27599-7420, USA

    D. Y. Lin

  2. School of Public Health and Community Medicine, Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195, USA

    P. J. Heagerty

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Robins, J.M. (2004). Optimal Structural Nested Models for Optimal Sequential Decisions. In: Lin, D.Y., Heagerty, P.J. (eds) Proceedings of the Second Seattle Symposium in Biostatistics. Lecture Notes in Statistics, vol 179. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9076-1_11

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  • DOI: https://doi.org/10.1007/978-1-4419-9076-1_11

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-20862-6

  • Online ISBN: 978-1-4419-9076-1

  • eBook Packages: Springer Book Archive

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