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A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients.

ABSTRACT: In traditional schedule or dose-schedule finding designs, patients are assumed to receive their assigned dose-schedule combination throughout the trial even though the combination may be found to have an undesirable toxicity profile, which contradicts actual clinical practice. Since no systematic approach exists to optimize intra-patient dose-schedule assignment, we propose a Phase I clinical trial design that extends existing approaches to optimize dose and schedule solely between patients by incorporating adaptive variations to dose-schedule assignments within patients as the study proceeds. Our design is based on a Bayesian non-mixture cure rate model that incorporates multiple administrations each patient receives with the per-administration dose included as a covariate. Simulations demonstrate that our design identifies safe dose and schedule combinations as well as the traditional method that does not allow for intra-patient dose-schedule reassignments, but with a larger number of patients assigned to safe combinations. Supplementary materials for this article are available online.

SUBMITTER: Zhang J

PROVIDER: S-EPMC3821780 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients.

Zhang Jin J Braun Thomas M TM

Journal of the American Statistical Association 20130101 503

In traditional schedule or dose-schedule finding designs, patients are assumed to receive their assigned dose-schedule combination throughout the trial even though the combination may be found to have an undesirable toxicity profile, which contradicts actual clinical practice. Since no systematic approach exists to optimize intra-patient dose-schedule assignment, we propose a Phase I clinical trial design that extends existing approaches to optimize dose and schedule solely between patients by i ...[more]

PMID: 24222927

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Project description:BackgroundIn phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size.MethodsTo balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage.ResultsFor a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data.ConclusionsBayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy within the context of limited resources. While the design is general enough to apply to many situations, future work is needed to address interim analyses and the incorporation of models for dose response.

Dataset Information

A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients.

Publications

A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients.

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