A Bayes-competing risk model for the use of expert judgment in reliability estimation

Coolen, Frank P. A.; Mertens, PR; Newby, MJ Martin

doi:10.1016/0951-8320(92)90018-g

Cited by 23 publications

(10 citation statements)

References 5 publications

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“…In another case, Laplace’s and Jeffreys’s priors are elicited to estimate a competing risks model with covariates [ 75 ]. Laplace’s prior has been considered for nonidentifiable model parameters, instead, Jeffreys’s prior has been considered for identifiable parameters [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Azzolina

Berchialla

Gregori

et al. 2021

IJERPH

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Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings.

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Section: Resultsmentioning

confidence: 99%

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Azzolina

Berchialla

Gregori

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…The prior probability distribution is one the major issues of the Bayesian theory. The usual practice is to use expert opinion regarding some reliability characteristics; 155,156 also, a meta-analysis can be used to quantify prior information in terms of distributions, 157 or a data-based prior distribution construction. 158 Finally, if no useful prior information is available, a non-informative prior or its proper approximation, which expresses general information about quantities of interest, can be adopted.…”

Section: Review Of the Current Shm Methodsmentioning

confidence: 99%

Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges

Vagnoli

Remenyte‐Prescott

Andrews

2017

Structural Health Monitoring

101

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“…More details about the advantages and disadvantages of subjective probability can be found in Chib et al 20 Although subjective information, in the form of prior distribution, can be very useful, its elicitation is a quite difficult and nontrivial task. The usual practice is to use expert opinion regarding some reliability characteristics 21,22 ; also, a meta-analysis can be used to quantify prior information in terms of distributions. 23 In addition, there exists in the literature different prior distribution construction approaches-data-based prior distribution construction, introduced by Guikema.…”

Section: Prior Information In Bayesian Modellingmentioning

confidence: 99%

Application of Bayesian Methods for Age‐Dependent Reliability Analysis

Alzbutas

Iešmantas

2013

Quality & Reliability Eng

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In this article, the authors present a general methodology for age‐dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age‐dependent change of beliefs about reliability parameters (mainly failure rate), when change of belief occurs not only because new failure data or other information becomes available with time but also because it continuously changes due to the flow of time and the evolution of beliefs. The main objective of this article is to present a clear way of how practitioners can apply Bayesian methods to deal with risk and reliability analysis considering ageing phenomena. The methodology describes step‐by‐step failure rate analysis of ageing components: from the Bayesian model building to its verification and generalization with Bayesian model averaging, which as the authors suggest in this article, could serve as an alternative for various goodness‐of‐fit assessment tools and as a universal tool to cope with various sources of uncertainty. The proposed methodology is able to deal with sparse and rare failure events, as is the case in electrical components, piping systems and various other systems with high reliability. In a case study of electrical instrumentation and control components, the proposed methodology was applied to analyse age‐dependent failure rates together with the treatment of uncertainty due to age‐dependent model selection. Copyright © 2013 John Wiley & Sons, Ltd.

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A Bayes-competing risk model for the use of expert judgment in reliability estimation

Cited by 23 publications

References 5 publications

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges

Application of Bayesian Methods for Age‐Dependent Reliability Analysis

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