Adjustment for non‐differential misclassification error in the generalized linear model

Xinhua Liu, Kung‐Yee ‐Y Liang

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)1197-1211
Number of pages15
JournalStatistics in Medicine
Volume10
Issue number8
DOIs
StatePublished - Aug 1991

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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