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Activity Number:
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153
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Type:
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Contributed
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Date/Time:
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Monday, August 12, 2002 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section*
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| Abstract - #300757 |
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Title:
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A Latent Variable Model for Measurement Error with a Skewed Regressor
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Author(s):
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Liang Li*+ and Mari Palta and Jun Shao
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Affiliation(s):
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University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison
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Address:
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1210 W Dayton St, Madison, Wisconsin, 53706, U.S.A.
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Keywords:
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Latent variable ; Measurement error ; Skewness ; longitudinal
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Abstract:
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In the study of measurement error models, it is typically assumed that the measurement error follows an additive or multiplicative model. However, such models do not hold for the measurement error of sleep-disordered breathing (SDB). The true covariate is severity of SDB, and the observed surrogate covariate is number of breathing pauses during unit time of sleep, which has a non-negative skewed distribution with point mass at zero. We propose a latent variable measurement error model to characterize the error structure in this situation and implement it into longitudinal and cross-sectional data analysis settings. A structural regression calibration method is used to estimate parameters. Our model demonstrates that in some situations, validation data are not needed for the measurement error model to be identifiable.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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