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Abstract
Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I–II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.
Original language | English |
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Article number | 2200288 |
Journal | Biometrical Journal |
Volume | 65 |
Issue number | 7 |
Early online date | 18 May 2023 |
DOIs | |
Publication status | Published - Oct 2023 |
Bibliographical note
Funding Information:Dr. Zheng's contribution to this manuscript was supported by Cancer Research UK (RCCPDF\100008).
Funding
Dr. Zheng's contribution to this manuscript was supported by Cancer Research UK (RCCPDF/100008).
Keywords
- drug combination
- information borrowing
- meta-analytic-combined
- phase I–II
- seamless designs
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Dive into the research topics of 'A Bayesian adaptive design for dual-agent phase I–II oncology trials integrating efficacy data across stages'. Together they form a unique fingerprint.Projects
- 1 Finished
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IDENT: Improving design and analysis of oncology trials Evaluating new Targeted Therapies
Zheng, H. (PI)
1/09/23 → 1/10/24
Project: UK charity