Premium
A cure‐rate model for Q‐learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients
Author(s) -
Moodie Erica E. M.,
Stephens David A.,
Alam Shomoita,
Zhang MeiJie,
Logan Brent,
Arora Mukta,
Spellman Stephen,
Krakow Elizabeth F.
Publication year - 2019
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201700181
Subject(s) - hematopoietic cell , medicine , cure rate , disease , curative treatment , haematopoiesis , oncology , transplantation , bone transplantation , bone marrow transplant , bone marrow transplantation , surgery , stem cell , biology , genetics
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.