Abstract
It is well known that estimates of association between an outcome variable and a set of categorical covariates, some of which are measured with misclassification, tend to be biased upon application of the usual methods of estimation that ignore the classification error. We propose a method to adjust for misclassification in covariates when one applies the generalized linear model. In the case where one can observe some true covariates only through surrogates, we combine a latent class analysis with the approach to incorporate multiple surrogates into the model. We include discussion on the efficacy of repeated measurements which one can view as a special case of multiple surrogates with identical distribution. We provide two examples to demonstrate the applicability of the method and the efficacy of multiple replicates for a covariate subject to misclassification in a regression framework.
Original language | English (US) |
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Pages (from-to) | 1197-1211 |
Number of pages | 15 |
Journal | Statistics in Medicine |
Volume | 10 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1991 |
Externally published | Yes |
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability