JSM 2005 - Toronto

Abstract #304406

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Activity Number: 258
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304406
Title: Designing Studies Involving Nongold Standard Diagnostic Tests
Author(s): Nandini Dendukuri*+ and Elham Rahme and Patrick Belisle and Lawrence Joseph
Companies: McGill University and McGill University and McGill University and McGill University
Address: R4.09 Ross Pavillion, Montreal, PQ, H3A 1A1, Canada
Keywords: Bayesian ; Sample size estimation ; Non-identifiability ; Diagnostic tests ; Sensitivity ; Specificity
Abstract:

Planning studies involving diagnostic tests is complicated by tests not always being accurate. Misclassification induced by imperfect tests must be accounted for, whether the goal is estimating disease prevalence or to investigate properties of a new test. Previous work on sample size requirements for estimating disease prevalence using a single, imperfect test showed large discrepancies in size compared to methods assuming a perfect test. We extend these methods to include two conditionally independent imperfect tests, and apply several criteria for Bayesian sample size determination to the design of such studies. We consider both disease prevalence studies and studies designed to estimate 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. Examples from infectious diseases are used for illustration.

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