A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients

Erica E.M. Moodie, David A. Stephens, Shomoita Alam, Mei Jie Zhang, Brent Logan, Mukta Arora, Stephen Spellman, Elizabeth F. Krakow

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations

    Abstract

    Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q-learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q-learning that fails to account for the cure-rate.

    Original languageEnglish (US)
    Pages (from-to)442-453
    Number of pages12
    JournalBiometrical Journal
    Volume61
    Issue number2
    DOIs
    StatePublished - Mar 2019

    Bibliographical note

    Funding Information:
    This work was funded by a grant from the Canadian Institutes of Health Research. We are grateful to the two anonymous referees and the associate editor whose thoughtful feedback helped to improve the clarity of our work.

    Keywords

    • Q-learning
    • adaptive treatment strategy
    • cure-rate
    • dynamic treatment regime
    • survival data

    PubMed: MeSH publication types

    • Journal Article
    • Research Support, N.I.H., Extramural
    • Research Support, Non-U.S. Gov't

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