A practical approach for eliciting expert prior beliefs about cancer survival in phase III randomized trial

Chevret, Sylvie; Lévy, Vincent

doi:10.1016/j.jclinepi.2008.04.009

Cited by 25 publications

(28 citation statements)

References 28 publications

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“…Using both 'enthusiastic' and 'skeptical' priors based on alternative and null testing hypotheses, respectively, for the log of hazard ratio with normal distribution has been proposed by Spiegelhalter et al [4] and Parmar et al [8]. Another method is to elicit the prior from multiple sources of information including experts' or investigators' opinions, historical or previous study results, or even recent external data obtained during the clinical trial [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of experts’ divergent prior beliefs for a sequential phase III clinical trial

et al. 2013

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Bayesian inference allows the quantification of increased survival in terms of probability distributions and provides investigators with an additional tool in the analysis of a randomized phase III clinical trial. Using a mixture of densities appears to be a promising strategy for incorporating the bimodal profile of prior opinion, with actualization of the two components along the trial as an illustration of the evolution of opinions as data are accumulated.

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Section: Introductionmentioning

confidence: 99%

“…The most popular method for eliciting expert prior distributions, known as 'opinion pools', consists of a weighted average of all or part of the experts' beliefs [9,12]. Some authors have also proposed a mixture of conjugate prior (either normal or beta) distributions to reflect the diversity of experts' opinions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Modeling of experts’ divergent prior beliefs for a sequential phase III clinical trial

et al. 2013

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“…The onus remains on clinical investigators to use belief-elicitation methods that have demonstrable methodologic rigor. In addition, Hiance et al have demonstrated that elicitation of prior beliefs is not only feasible, but allows for insights to be gained into the variability of experts' beliefs [66]. Consideration of a variety of prior distributions allows for the approximation of the posterior distributions held by all types of readers [66].…”

Section: Normative Goodnessmentioning

confidence: 99%

“…In addition, Hiance et al have demonstrated that elicitation of prior beliefs is not only feasible, but allows for insights to be gained into the variability of experts' beliefs [66]. Consideration of a variety of prior distributions allows for the approximation of the posterior distributions held by all types of readers [66]. They suggest that elicitation from a set of experts should be considered as part of the design of future trials [66].…”

Section: Normative Goodnessmentioning

confidence: 99%

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Methods to elicit beliefs for Bayesian priors: a systematic review

Johnson

Tomlinson

Hawker

et al. 2010

Journal of Clinical Epidemiology

161

163

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Assurance methods for designing a clinical trial with a delayed treatment effect

Salsbury,

Oakley,

Julious

et al. 2024

Statistics in Medicine

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An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno‐oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open‐source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno‐oncology and for other treatments where a delayed treatment effect is expected to occur.

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A practical approach for eliciting expert prior beliefs about cancer survival in phase III randomized trial

Cited by 25 publications

References 28 publications

Modeling of experts’ divergent prior beliefs for a sequential phase III clinical trial

Modeling of experts’ divergent prior beliefs for a sequential phase III clinical trial

Methods to elicit beliefs for Bayesian priors: a systematic review

Assurance methods for designing a clinical trial with a delayed treatment effect

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