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

David Rindskopf; Shiela Strauss

doi:10.1207/S15328007SEM1101_4

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

David Rindskopf, Shiela Strauss

Nursing

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	51-59
Number of pages	9
Journal	Structural Equation Modeling
Volume	11
Issue number	1
DOIs	https://doi.org/10.1207/S15328007SEM1101_4
State	Published - 2004

ASJC Scopus subject areas

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

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10.1207/S15328007SEM1101_4

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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.",

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

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