Bayesian techniques for sample size determination in clinical trials: a short review

Pezeshk, Hamid

doi:10.1191/0962280203sm345oa

Cited by 67 publications

(44 citation statements)

References 28 publications

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“…Following Pezeshk (2003), let us present some definitions. r Definition 1: A real-valued and nonnegative function L(θ , a) defined by L : × A → [0, ∞), in which = ∅ is the parameter space and A = ∅ is a set of actions (or decisions), is called the loss function.…”

Section: Expected Value Of Sample Informationmentioning

confidence: 99%

An Optimization Approach to Calculating Sample Sizes With Binary Responses

Maroufy

Marriott

Pezeshk

2014

Journal of Biopharmaceutical Statistics

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In this article, we discuss an optimization approach to the sample size question, founded on maximizing the value of information in comparison studies with binary responses. The expected value of perfect information (EVPI) is calculated and the optimal sample size is obtained by maximizing the expected net gain of sampling (ENGS), the difference between the expected value of sample information (EVSI) and the cost of conducting the trial. The data are assumed to come from two independent binomial distributions, while the parameter of interest is the difference between the two success probabilities, [Formula: see text]. To formulate our prior knowledge on the parameters, a Dirichlet prior is used. Monte Carlo integration is used in the computation and optimization of ENGS. We also compare the results of this approach with existing Bayesian methods and show how the new approach reduces the computational complexity considerably.

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Section: Expected Value Of Sample Informationmentioning

confidence: 99%

An Optimization Approach to Calculating Sample Sizes With Binary Responses

Maroufy

Marriott

Pezeshk

2014

Journal of Biopharmaceutical Statistics

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“…A hierarchical logistic regression was used for binary longitudinal outcomes in a trial of severe chronic constipation with non-linear treatment effects, heterogeneity and a high proportion of non-responders [462]. In addition, reviews in Statistical Methods in Medical Research showed different approaches to Bayesian sample size determination [463]; approaches to phase II design were explored [464], as were approaches to multi-centre trials [465]. There was still relatively little about safety, but multi-item gamma Poisson shrinkage was used to analyse vaccine adverse event reports [466].…”

Section: Clinical Trialsmentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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“…(A review is provided by Pezeshk [20].) In particular, most authors have proposed using a Bayesian decision theoretic approach, introduced by Grundy et al [21] and developed by Rai a and Schlaifer [22], to determine the sample size that maximizes the di erence between the cost of the trial and the expected value of the results.…”

Section: Introductionmentioning

confidence: 99%

The value of information and optimal clinical trial design

2005

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Traditional sample size calculations for randomized clinical trials depend on somewhat arbitrarily chosen factors, such as type I and II errors. Type I error, the probability of rejecting the null hypothesis of no difference when it is true, is most often set to 0.05, regardless of the cost of such an error. In addition, the traditional use of 0.2 for the type II error means that the money and effort spent on the trial will be wasted 20 per cent of the time, even when the true treatment difference is equal to the smallest clinically important one and, again, will not reflect the cost of making such an error. An effectiveness trial (otherwise known as a pragmatic trial or management trial) is essentially an effort to inform decision-making, i.e. should treatment be adopted over standard? As such, a decision theoretic approach will lead to an optimal sample size determination. Using incremental net benefit and the theory of the expected value of information, and taking a societal perspective, it is shown how to determine the sample size that maximizes the difference between the cost of doing the trial and the value of the information gained from the results. The methods are illustrated using examples from oncology and obstetrics.

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Bayesian techniques for sample size determination in clinical trials: a short review

Cited by 67 publications

References 28 publications

An Optimization Approach to Calculating Sample Sizes With Binary Responses

An Optimization Approach to Calculating Sample Sizes With Binary Responses

Bayesian statistics in medicine: a 25 year review

The value of information and optimal clinical trial design

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