Adaptive Design of Personalized Dose-Finding Clinical Trials

Saeid Delshad () and Amin Khademi ()
Additional contact information
Saeid Delshad: Department of Industrial Engineering, Clemson University, Clemson, South Carolina 29631
Amin Khademi: Department of Industrial Engineering, Clemson University, Clemson, South Carolina 29631

Service Science, 2022, vol. 14, issue 4, 273-291

Abstract: A key and challenging step toward personalized/precision medicine is the ability to redesign dose-finding clinical trials. This work studies a problem of fully response-adaptive Bayesian design of phase II dose-finding clinical trials with patient information, where the decision maker seeks to identify the right dose for each patient type (often defined as an effective target dose for each group of patients) by minimizing the expected (over patient types) variance of the right dose. We formulate this problem by a stochastic dynamic program and exploit a few properties of this class of learning problems. Because the optimal solution is intractable, we propose an approximate policy by an adaptation of a one-step look-ahead framework. We show the optimality of the proposed policy for a setting with homogeneous patients and two doses and find its asymptotic rate of sampling. We adapt a number of commonly applied allocation policies in dose-finding clinical trials, such as posterior adaptive sampling, and test their performance against our proposed policy via extensive simulations with synthetic and real data. Our numerical analyses provide insights regarding the connection between the structure of the dose-response curve for each patient type and the performance of allocation policies. This paper provides a practical framework for the Food and Drug Administration and pharmaceutical companies to transition from the current phase II procedures to the era of personalized dose-finding clinical trials.

Keywords: adaptive dose-finding; clinical trials; precision medicine; one-step look-ahead policy (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/serv.2022.0306 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:14:y:2022:i:4:p:273-291

Access Statistics for this article

More articles in Service Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().