A cure‐rate model for Q‐learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients. Issue 2 (16th May 2018)

• Record Type: Journal Article
• Authors: Moodie, Erica E. M.; Stephens, David A.; Alam, Shomoita; Zhang, Mei‐Jie; Logan, Brent; Arora, Mukta; Spellman, Stephen; Krakow, Elizabeth F.
• Is Part Of: Biometrical journal. Volume 61:Issue 2(2019:Mar.)
• Page Start: 442
• Page End: 453
• Publication Date: 2018-05-16
• Subjects: adaptive treatment strategy -- cure‐rate -- dynamic treatment regime -- survival data -- Q‐learning; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201700181
• Languages: English
• ISSNs: 0323-3847

Abstract

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.