An official website of the United States government
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 .
more »
« less
Optimal Stopping of Adaptive Dose-Finding Trials
The primary objective of this paper is to develop computationally efficient methods for optimal stopping of an adaptive Phase II dose-finding clinical trial, where the decision maker may terminate the trial for efficacy or abandon it as a result of futility. We develop two solution methods and compare them in terms of computational time and several performance metrics such as the probability of correct stopping decision. One proposed method is an application of the one-step look-ahead policy to this problem. The second proposal builds a diffusion approximation to the state variable in the continuous regime and approximates the trial’s stopping time by optimal stopping of a diffusion process. The secondary objective of the paper is to compare these methods on different dose-response curves, particularly when the true dose-response curve has no significant advantage over a placebo. Our results, which include a real clinical trial case study, show that look-ahead policies perform poorly in terms of the probability of correct decision in this setting, whereas our diffusion approximation method provides robust solutions.
more »
« less
- Award ID(s):
- 1651912
- PAR ID:
- 10209407
- Date Published:
- Journal Name:
- Service Science
- Volume:
- 12
- Issue:
- 2-3
- ISSN:
- 2164-3962
- Page Range / eLocation ID:
- 80 to 99
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
ABSTRACT We develop a new framework specifically for early Phase I clinical trials called Bayesian Ordered Lattice Design (BOLD). This study is motivated by two key factors. First, Phase I clinical trials typically involve relatively small sample sizes, which can make the use of prior information on dose‐limiting toxicity (DLT) rates highly significant. To address this challenge, the proposed Bayesian methodology incorporates prior information and posterior updating to guide dose selection, toxicity monitoring, early stopping, and identification of the maximum tolerable dose (MTD). Second, a natural ordering among toxicity probabilities across different dose levels can be utilized, with the idea being that analysis of dose‐level posterior probabilities can and should acquire insights from data obtained at other dose levels, by leveraging their order relationship. Our proposed approach employs straightforward dose‐level Bayesian specifications and relies on intuitive and clinically interpretable DLT rate posterior probabilities for decision‐making. Importantly, we show that it can often outperform popular methods in terms of accuracy in determining the MTD. This Bayesian approach is also computationally simple and avoids simulation.more » « less
-
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.more » « less
-
Abstract This paper develops a hybrid deep reinforcement learning approach to manage an insurance portfolio for diffusion models. To address the model uncertainty, we adopt the recently developed modelling of exploration and exploitation strategies in a continuous-time decision-making process with reinforcement learning. We consider an insurance portfolio management problem in which an entropy-regularized reward function and corresponding relaxed stochastic controls are formulated. To obtain the optimal relaxed stochastic controls, we develop a Markov chain approximation and stochastic approximation-based iterative deep reinforcement learning algorithm where the probability distribution of the optimal stochastic controls is approximated by neural networks. In our hybrid algorithm, both Markov chain approximation and stochastic approximation are adopted in the learning processes. The idea of using the Markov chain approximation method to find initial guesses is proposed. A stochastic approximation is adopted to estimate the parameters of neural networks. Convergence analysis of the algorithm is presented. Numerical examples are provided to illustrate the performance of the algorithm.more » « less
-
An immunotherapy trial often uses the phase I/II design to identify the optimal biological dose, which monitors the efficacy and toxicity outcomes simultaneously in a single trial. The progression-free survival rate is often used as the efficacy outcome in phase I/II immunotherapy trials. As a result, patients developing disease progression in phase I/II immunotherapy trials are generally seriously ill and are often treated off the trial for ethical consideration. Consequently, the happening of disease progression will terminate the toxicity event but not vice versa, so the issue of the semi-competing risks arises. Moreover, this issue can become more intractable with the late-onset outcomes, which happens when a relatively long follow-up time is required to ascertain progression-free survival. This paper proposes a novel Bayesian adaptive phase I/II design accounting for semi-competing risks outcomes for immunotherapy trials, referred to as the dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials (SCI) design. To tackle the issue of the semi-competing risks in the presence of late-onset outcomes, we re-construct the likelihood function based on each patient's actual follow-up time and develop a data augmentation method to efficiently draw posterior samples from a series of Beta-binomial distributions. We propose a concise curve-free dose-finding algorithm to adaptively identify the optimal biological dose using accumulated data without making any parametric dose–response assumptions. Numerical studies show that the proposed SCI design yields good operating characteristics in dose selection, patient allocation, and trial duration.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1287/serv.2020.0261
