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 |
| State | Published - 2004 |
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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 journal › Article
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TY - JOUR
T1 - Determining predictors of true HIV status using an errors-in-variables model with missing data
AU - Rindskopf, David
AU - Strauss, Shiela
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=2642579194&partnerID=8YFLogxK
M3 - Article
VL - 11
SP - 51
EP - 59
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 1
ER -