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Article: Robust Bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities

TitleRobust Bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities
Authors
KeywordsMisclassification
Robust Bayes
Case–control study
Bayesian
Issue Date2015
PublisherWalter de Gruyter GmbH. The Journal's web site is located at https://www.degruyter.com/view/j/ijb
Citation
The International Journal of Biostatistics, 2015, v. 11 n. 1, p. 135-149 How to Cite?
AbstractExposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.
Persistent Identifierhttp://hdl.handle.net/10722/214691
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.534
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMak, TSH-
dc.contributor.authorBest, N-
dc.contributor.authorRushton, L-
dc.date.accessioned2015-08-21T11:51:48Z-
dc.date.available2015-08-21T11:51:48Z-
dc.date.issued2015-
dc.identifier.citationThe International Journal of Biostatistics, 2015, v. 11 n. 1, p. 135-149-
dc.identifier.issn1557-4679-
dc.identifier.urihttp://hdl.handle.net/10722/214691-
dc.description.abstractExposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.-
dc.languageeng-
dc.publisherWalter de Gruyter GmbH. The Journal's web site is located at https://www.degruyter.com/view/j/ijb-
dc.relation.ispartofThe International Journal of Biostatistics-
dc.rights© 2015 Walter de Gruyter GmbH, Berlin/Boston. The final publication is available at www.degruyter.com-
dc.subjectMisclassification-
dc.subjectRobust Bayes-
dc.subjectCase–control study-
dc.subjectBayesian-
dc.titleRobust Bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities-
dc.typeArticle-
dc.identifier.emailMak, TSH: tshmak@hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1515/ijb-2013-0044-
dc.identifier.pmid25720128-
dc.identifier.scopuseid_2-s2.0-84928893410-
dc.identifier.hkuros248864-
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.spage135-
dc.identifier.epage149-
dc.identifier.isiWOS:000353650900008-
dc.publisher.placeGermany-
dc.identifier.issnl1557-4679-

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