Bayesian designs with frequentist and Bayesian error rate considerations

Wang, You‐Gan; Leung, Denis H. Y.; Li, Manning; Tan, Shan

doi:10.1191/0962280205sm410oa

Cited by 25 publications

(22 citation statements)

References 14 publications

(17 reference statements)

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“…Several similar approaches of taking the Bayesian framework to achieve good frequentist properties have been proposed in the literature. [43,44] We advocate the use of extensive computation and/or simulation studies to search for the optimal design parameters and to evaluate the design's operating characteristics. Sensitivity analysis should be conducted to examine how the design parameters influence the design properties.…”

Section: Discussionmentioning

confidence: 99%

A predictive probability design for phase II cancer clinical trials

2008

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Background-Two-or three-stage designs are commonly used in phase II cancer clinical trials. These designs possess good frequentist properties and allow early termination of the trial when the interim data indicate that the experimental regimen is inefficacious. The rigid study design, however, can be difficult to follow exactly because the response has to be evaluated at prespecified fixed number of patients.Purpose-Our goal is to develop an efficient and flexible design that possesses desirable statistical properties.Method-A flexible design based on Bayesian predictive probability and the minimax criterion is constructed. A three-dimensional search algorithm is implemented to determine the design parameters.Results-The new design controls type I and type II error rates, and allows continuous monitoring of the trial outcome. Consequently, under the null hypothesis when the experimental treatment is not efficacious, the design is more efficient in stopping the trial earlier, which results in a smaller expected sample size. Exact computation and simulation studies demonstrate that the predictive probability design possesses good operating characteristics.Limitations-The predictive probability design is more computationally intensive than two-or three-stage designs. Similar to all designs with early stopping due to futility, the resulting estimate of treatment efficacy may be biased.Conclusions-The predictive probability design is efficient and remains robust in controlling type I and type II error rates when the trial conduct deviates from the original design. It is more adaptable than traditional multi-stage designs in evaluating the study outcome, hence, it is easier to implement. S-PLUS/R programs are provided to assist the study design.

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Section: Discussionmentioning

confidence: 99%

A predictive probability design for phase II cancer clinical trials

2008

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“…The majority of them are extensions in a randomized fashion of popular single-arm designs for phase II of clinical trials based on a frequentist approach (see the textbook by Jung, 2013, for a review). The development of randomized versions of Bayesian single-arm two-stage designs previously proposed (see, for instance, Tan and Machin, 2002;Wang et al, 2005;Sambucini, 2010) could be an interesting topic for future research.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Single-Arm vs. Randomized Phase II Clinical Trials: A Bayesian Approach

Sambucini

2014

Journal of Biopharmaceutical Statistics

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Single-arm studies are typically used in phase II of clinical trials, whose main objective is to determine whether a new treatment warrants further testing in a randomized phase III trial. The introduction of randomization in phase II, to avoid the limits of studies based on historical controls, is a critical issue widely debated in the recent literature. We use a Bayesian approach to compare single-arm and randomized studies, based on a binary response variable, in terms of their abilities of reaching the correct decision about the new treatment, both when it performs better than the standard one and when it is less effective. We evaluate how the historical control rate, the total sample size, and the elicitation of the prior distributions affect the decision about which study performs better.

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“…This study was also used by Tan and Machin [9] and Wang et al [12] to illustrate their Bayesian designs. In the original trial, the number of patients to be enrolled in the two stages was found using the Simon's minimax design.…”

Section: An Illustrative Examplementioning

confidence: 95%

A Bayesian predictive two‐stage design for phase II clinical trials

Sambucini

2008

Statistics in Medicine

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In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.

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Bayesian designs with frequentist and Bayesian error rate considerations

Cited by 25 publications

References 14 publications

A predictive probability design for phase II cancer clinical trials

A predictive probability design for phase II cancer clinical trials

Comparison of Single-Arm vs. Randomized Phase II Clinical Trials: A Bayesian Approach

A Bayesian predictive two‐stage design for phase II clinical trials

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