Determining predictors of true HIV status using an errors-in-variables model with missing data

David Rindskopf, Shiela Strauss

Research output: Contribution to journalArticlepeer-review

Abstract

We demonstrate a model for categorical data that parallels the MIMIC model for continuous data. The model is equivalent to a latent class model with observed covariates; further, it includes simple handling of missing data. The model is used on data from a large-scale study of HIV that had both biological measures of infection and self-report (missing on some cases). The model allows the determination of sensitivity and specificity of each measure, and an assessment of how well true HIV status can be predicted from characteristics of the individuals in the study.

Original languageEnglish (US)
Pages (from-to)51-59
Number of pages9
JournalStructural Equation Modeling
Volume11
Issue number1
DOIs
StatePublished - 2004

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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