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Estimating odds ratios adjusting for misclassification in Alzheimer's disease risk factor assessment
Author(s) -
Emsley Christine L.,
Gao Sujuan,
Hall Kathleen S.,
Hendrie Hugh C.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000615/30)19:11/12<1523::aid-sim442>3.0.co;2-l
Subject(s) - estimator , statistics , odds ratio , dementia , odds , missing data , standard error , estimation , risk factor , computer science , econometrics , medicine , disease , mathematics , logistic regression , management , economics
Epidemiological studies of Alzheimer's disease and dementia are often two‐phase studies including a screening phase and a clinical assessment phase. It is common to interview a relative of the subject at each of these phases to obtain information about the subject's exposure to risk factors. This can result in a misclassification error when assessing risk factors, as the two responses of the relative often differ. This is especially a problem for risk factors involving life‐style and family history which cannot be confirmed using the subject's medical records. A naive analysis using data from each phase separately would give two different estimates of the odds ratio; both estimates could be biased. In this paper, we extend the estimation methods adjusting for misclassification developed by Liu and Liang to data collected through two‐phase sampling. We first use a latent class analysis and the EM algorithm to estimate the misclassification parameters. We then derive the maximum pseudo‐likelihood estimators, conditional on the misclassification parameters, to estimate the odds ratios accounting for the complex sampling study design. We propose to use the jack‐knife estimator for estimation of the variances. We apply the above method to data collected in the Indianapolis–Ibadan Dementia Study to estimate the odds ratio for smoking adjusting for misclassification error. Copyright © 2000 John Wiley & Sons, Ltd.