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

Journal: Biometrical journal. Biometrische Zeitschrift
Published Date: Mar 1, 2019

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.

Authors

  • Erica E M Moodie
    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, H3A 1A2, Canada.
  • David A Stephens
    Department of Mathematics and Statistics, McGill University, Montreal, QC, H3A 1A2, Canada.
  • Shomoita Alam
    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, H3A 1A2, Canada.
  • Mei-Jie Zhang
    Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
  • Brent Logan
    Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226.
  • Mukta Arora
    Department of Medicine, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Stephen Spellman
    Center for International Blood and Marrow Transplant Research, Minneapolis, MN, 55401, USA.
  • Elizabeth F Krakow
    Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.

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