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. Funding: This research is supported by the National Science Foundation [Grant 1651912]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2022.0306 .
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Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials
Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in “the intersection of computing and operations research” as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials.
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- Award ID(s):
- 1651912
- PAR ID:
- 10316500
- Date Published:
- Journal Name:
- INFORMS Journal on Computing
- ISSN:
- 1091-9856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/ijoc.2021.1082
