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Dynamic treatment regimes for managing chronic health conditions: A statistical perspective

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  • Chakraborty, B.

Abstract

Dynamic treatment regimes are an emerging and important methodological area in health research, particularly in the management of chronic health conditions. This paradigm encompasses the ideological shift in research from the acute care model to the chronic care model. It allows individualization of treatment (type, dosage, timing) at each stage of intervention. Constructing evidence-based dynamic treatment regimes requires implementation of cuttingedge design and analysis tools. Here I briefly discuss some of these modern tools, namely the sequential multiple assignment randomized trial (SMART) design and a regression-based analysis approach called Q-learning.

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  • Chakraborty, B., 2011. "Dynamic treatment regimes for managing chronic health conditions: A statistical perspective," American Journal of Public Health, American Public Health Association, vol. 101(1), pages 40-45.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2010.198937_4
    DOI: 10.2105/AJPH.2010.198937
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    Cited by:

    1. Erica E. M. Moodie & Janie Coulombe & Coraline Danieli & Christel Renoux & Susan M. Shortreed, 2022. "Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 512-542, July.

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