Document Detail


Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test.
MedLine Citation:
PMID:  15180664     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Planning studies involving diagnostic tests is complicated by the fact that virtually no test provides perfectly accurate results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be taken into account, whether the primary goal of the study is to estimate the prevalence of a disease in a population or to investigate the properties of a new diagnostic test. Previous work on sample size requirements for estimating the prevalence of disease in the case of a single imperfect test showed very large discrepancies in size when compared to methods that assume a perfect test. In this article we extend these methods to include two conditionally independent imperfect tests, and apply several different criteria for Bayesian sample size determination to the design of such studies. We consider both disease prevalence studies and studies designed to estimate the sensitivity and specificity of diagnostic tests. As the problem is typically nonidentifiable, we investigate the limits on the accuracy of parameter estimation as the sample size approaches infinity. Through two examples from infectious diseases, we illustrate the changes in sample sizes that arise when two tests are applied to individuals in a study rather than a single test. Although smaller sample sizes are often found in the two-test situation, they can still be prohibitively large unless accurate information is available about the sensitivities and specificities of the tests being used.
Authors:
Nandini Dendukuri; Elham Rahme; Patrick Bélisle; Lawrence Joseph
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  60     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2004 Jun 
Date Detail:
Created Date:  2004-06-07     Completed Date:  2005-01-14     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  388-97     Citation Subset:  IM    
Affiliation:
Department of Epidemiology and Biostatistics, 1020 Pine Avenue West, McGill University, Montreal, Québec H3A 1A2, Canada. nandini.dendukuri@mcgill.ca
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Biometry*
Chlamydia Infections / diagnosis,  epidemiology
Chlamydia trachomatis
Humans
Male
Sample Size*
Sensitivity and Specificity
Strongyloidiasis / diagnosis,  epidemiology

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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