Patient-type Bayes-adaptive treatment plans
File(s)
Accepted version
Author(s)
Skandari, Mohammadreza
Shechter, Steven
Type
Journal Article
Abstract
Patient heterogeneity in disease progression is prevalent in many settings. Treatment decisions that explic-itly consider this heterogeneity can lower the cost of care and improve outcomes by providing the right carefor the right patient at the right time. In this paper, we analyze the problem of designing ongoing treat-ment plans for a population with heterogeneity in disease progression and response to medical interventions.We create a model that learns the patient type by monitoring the patient health over time and updates apatient’s treatment plan according to the gathered information. We formulate the problem as a multivariatestate-space, partially observable Markov decision process (POMDP) and provide structural properties ofthe value function, as well as the optimal policy. We extend this modeling framework to a general class oftreatment initiation problems where there is a stochastic lead-time before a treatment becomes available oreffective. As a case study, we develop a data-driven, decision-analytic model to study the optimal timing ofvascular access surgery for patients with progressive chronic kidney disease, and we establish policies thatconsider a patient’s rate of disease progression in addition to the kidney health state. To circumvent thecurse of dimensionality of the POMDP, we develop several approximate policies, as well as simpler heuristics,and evaluate them against a high-quality lower-bound. Through a numerical study and several sensitivityanalyses, we establish the high quality and robustness of an approximate policy that we develop. We providefurther policy insights that sharpen existing guidelines for the case-study problem.
Date Issued
2021-03-01
Date Acceptance
2020-02-14
Citation
Operations Research, 2021, 69 (2), pp.574-598
ISSN
0030-364X
Publisher
Institute for Operations Research and Management Sciences
Start Page
574
End Page
598
Journal / Book Title
Operations Research
Volume
69
Issue
2
Copyright Statement
Copyright © 2021, INFORMS. This document is the Accepted Manuscript version of a published work that appeared in final form in Operations Research, vol. 69 issue 2, https://doi.org/10.1287/opre.2020.2011
Identifier
https://pubsonline.informs.org/doi/10.1287/opre.2020.2011
Subjects
0102 Applied Mathematics
0802 Computation Theory and Mathematics
1503 Business and Management
Operations Research
Publication Status
Published
Date Publish Online
2021-02-15