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

David Rindskopf, Shiela Strauss

Research output: Contribution to journalArticle

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
StatePublished - 2004

Fingerprint

Errors-in-variables Model
Missing Data
Predictors
HIV
Self Report
Sensitivity and Specificity
Latent Class Model
Infection
Model
Nominal or categorical data
Specificity
Covariates
Errors in variables
Missing data
Demonstrate

ASJC Scopus subject areas

  • Psychology(all)
  • Sociology and Political Science
  • Education
  • Political Science and International Relations
  • Economics, Econometrics and Finance(all)

Cite this

Determining predictors of true HIV status using an errors-in-variables model with missing data. / Rindskopf, David; Strauss, Shiela.

In: Structural Equation Modeling, Vol. 11, No. 1, 2004, p. 51-59.

Research output: Contribution to journalArticle

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