Modeling sensitivity and specificity with a time-varying reference standard within a longitudinal setting

Qin Yu, Wan Tang, Sue Marcus, Yan Ma, Hui Zhang, Xin Tu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Diagnostic tests are used in a wide range of behavioral, medical, psychosocial, and healthcare-related research. Test sensitivity and specificity are the most popular measures of accuracy for diagnostic tests. Available methods for analyzing longitudinal study designs assume fixed gold or reference standards and as such do not apply to studies with dynamically changing reference standards, which are especially popular in psychosocial research. In this article, we develop a novel approach to address missing data and other related issues for modeling sensitivity and specificity within such a time-varying reference standard setting. The approach is illustrated with real as well as simulated data.

Original languageEnglish (US)
Pages (from-to)1213-1230
Number of pages18
JournalJournal of Applied Statistics
Volume37
Issue number7
DOIs
StatePublished - Jul 2010

Keywords

  • Augmented inverse probability weighted (AIPW) estimate
  • Bivariate monotone missing data pattern (BMMDP)
  • Diagnostic test
  • Double robust estimate
  • Inverse probability weighted (IPW) estimate
  • Missing data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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