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Summary A sequentially adaptive Bayesian design is presented for a clinical trial of cord-blood-derived natural killer cells to treat severe haematologic malignancies. Given six prognostic subgroups defined by disease type and severity, the goal is to optimize cell dose in each subgroup. The trial has five co-primary outcomes: the times to severe toxicity, cytokine release syndrome, disease progression or response and death. The design assumes a multivariate Weibull regression model, with marginals depending on dose, subgroup and patient frailties that induce association between the event times. Utilities of all possible combinations of the non-fatal outcomes over the first 100 days following cell infusion are elicited, with posterior mean utility used as a criterion to optimize the dose. For each subgroup, the design stops accrual to doses having an unacceptably high death rate and at the end of the trial selects the optimal safe dose. A simulation study is presented to validate the design's safety, ability to identify optimal doses and robustness, and to compare it with a simplified design that ignores patient heterogeneity.
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SCI : A Bayesian adaptive phase I/ II dose‐finding design accounting for semi‐competing risks outcomes for immunotherapy trials
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.
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- PAR ID:
- 10320024
- Date Published:
- Journal Name:
- Pharmaceutical Statistics
- ISSN:
- 1539-1604
- 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/10.1002/pst.2209
