IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v6y2010i2n10.html

Optimal Dynamic Regimes: Presenting a Case for Predictive Inference

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1204
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1204?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hirano, Keisuke & Porter, Jack R., 2020. "Asymptotic analysis of statistical decision rules in econometrics," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 283-354, Elsevier.
    2. Dan Liu & Wenqing He, 2026. "Dynamic Treatment Regimes with Replicated Observations Available for Error-Prone Covariates: A Q-Learning Approach," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 18(1), pages 21-45, March.
    3. Xiao Li & Brent R. Logan & S. M. Ferdous Hossain & Erica E. M. Moodie, 2024. "Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 181-212, January.
    4. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2016. "Tackling non-ignorable dropout in the presence of time varying confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 775-795, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1, September.
    2. Stephens Alisa & Keele Luke & Joffe Marshall, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-17, September.
    3. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
    4. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.
    5. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects From Spillover, Interaction, and General Equilibrium Effects," Rationality and Competition Discussion Paper Series 21, CRC TRR 190 Rationality and Competition.
    6. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    7. Justin Whitehouse & Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2025. "Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing," Papers 2507.11780, arXiv.org, revised Mar 2026.
    8. Michael R. Elliott & Anna Conlon & Yun Li, 2013. "Discussion on “Surrogate Measures and Consistent Surrogates”," Biometrics, The International Biometric Society, vol. 69(3), pages 565-569, September.
    9. Ying Huang & Shibasish Dasgupta, 2019. "Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 504-523, December.
    10. Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
    11. Jin Wang & Donglin Zeng & D. Y. Lin, 2022. "Semiparametric single-index models for optimal treatment regimens with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 744-763, October.
    12. Deuchert, Eva & Eugster, Beatrix, 2019. "Income and substitution effects of a disability insurance reform," Journal of Public Economics, Elsevier, vol. 170(C), pages 1-14.
    13. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    14. Patrick M. Schnell & Richard Baumgartner & Shahrul Mt‐Isa & Vladimir Svetnik, 2022. "A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1065-1084, November.
    15. Ji Liu, 2024. "Education legislations that equalize: a study of compulsory schooling law reforms in post-WWII United States," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    16. Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    17. Thomas R. Ten Have & Marshall M. Joffe & Kevin G. Lynch & Gregory K. Brown & Stephen A. Maisto & Aaron T. Beck, 2007. "Causal Mediation Analyses with Rank Preserving Models," Biometrics, The International Biometric Society, vol. 63(3), pages 926-934, September.
    18. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    19. Ayush Sawarni & Jikai Jin & Justin Whitehouse & Vasilis Syrgkanis, 2025. "Policy Learning with Abstention," Papers 2510.19672, arXiv.org, revised Jan 2026.
    20. John Engberg & Dennis Epple & Jason Imbrogno & Holger Sieg & Ron Zimmer, 2014. "Evaluating Education Programs That Have Lotteried Admission and Selective Attrition," Journal of Labor Economics, University of Chicago Press, vol. 32(1), pages 27-63.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.