Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data

Chen, Ming Hui; Ibrahim, Joseph G.; Lam, Peter; Yu, Alan; Zhang, Yuanye

doi:10.1111/j.1541-0420.2011.01561.x

Cited by 73 publications

(102 citation statements)

References 34 publications

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“…Note that the forms of (3) and (6) imply that the synthesis approach is always more efficient than that of the fixed margin test. The comparison between the two frequentist approaches becomes more subtle as the Type I error associated with each test is different.…”

Section: Synthesis Approachmentioning

confidence: 99%

“…Again based on Bayes factor, they have proposed a hybrid Bayesian/frequentist approach, which uses Bayes factor as a measure of evidence against the null hypothesis while controlling both Type I and II errors for determining a cut-off value for decision-making. It is straightforward to extend these models for continuous outcome using the proper link function (Chen, 2011).…”

Section: Common Bayesian Approachesmentioning

confidence: 99%

“…† MI=mildly informative e.g. Beta(4,6). NA is for "not applicable".Downloaded by [University of Sussex Library] at 05:47 11 August 2015 Downloaded by [University of Sussex Library] at 05:47 11 August 2015…”

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confidence: 99%

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Bayesian methods for the design and analysis of noninferiority trials

Gamalo

Gao

Lakshminarayanan

et al. 2016

Journal of Biopharmaceutical Statistics

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The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.

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Section: Synthesis Approachmentioning

confidence: 99%

Section: Common Bayesian Approachesmentioning

confidence: 99%

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Bayesian methods for the design and analysis of noninferiority trials

Gamalo

Gao

Lakshminarayanan

et al. 2016

Journal of Biopharmaceutical Statistics

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“…Instead of superiority as the alternative hypothesis, the null hypothesis is inferiority by at least a pre-specified amount d, the noninferiority margin, and the alternative is non-inferiority by more than d. In a two-arm non-inferiority trial of an experimental product to a known active control, a Bayesian approach seems most natural, incorporating prior information explicitly from earlier studies of the active control to a placebo control. Interesting Bayesian papers on non-inferiority are Chen et al (2011) using historical controls for medical devices and Gamalo et al (2014) for non-infective drug products.…”

Section: Non-inferiority and Bayesmentioning

confidence: 99%

Bayesian methods in clinical trials with applications to medical devices

Campbell

2017

CSAM

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Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

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“…Under this approach, less information will be borrowed from other drugs if the between-drug variability is high. Another approach is to use power likelihood (Chen et al 2011;Ibrahim et al 2012;Ibrahim and Chen 2000) for the other drugs.…”

Section: Introductionmentioning

confidence: 99%