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Updated: Aug 12, 2026

High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Product limit estimation for infectious disease data when the diagnostic test for the outcome is measured with
1Department of Biostatistics, University of Washington, Seattle 98195, USA. barbrar@u.washington.edu
Abstract:
Low sensitivity and/or specificity of a diagnostic test for outcome results in biased estimates of the time to first event using product limit estimation. For example, if a test has low specificity, estimates of the cumulative distribution function (cdf) are biased towards time zero, while estimates of the cdf are biased away from time zero if a test has low sensitivity. In the context of discrete time survival analysis for infectious disease data, we develop self-consistent algorithms to obtain unbiased estimates of the time to first event when the sensitivity and/or specificity of the diagnostic test for the outcome is less than 100%. Two examples are presented. The first involves estimating time to first detection of HIV-1 infection in infants in a randomized clinical trial, and the second involves estimating time to first Neisseria gonorrhoeae infection in a cohort of Kenyan prostitutes.
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