Abstract
Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.
Original language | English (US) |
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Pages (from-to) | 692-706 |
Number of pages | 15 |
Journal | Pharmaceutical statistics |
Volume | 22 |
Issue number | 4 |
DOIs | |
State | Published - Jul 1 2023 |
Keywords
- Bayesian design
- cell therapy
- dose finding
- phase I-II clinical trial
ASJC Scopus subject areas
- Statistics and Probability
- Pharmacology
- Pharmacology (medical)
MD Anderson CCSG core facilities
- Biostatistics Resource Group