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Correlated and misclassified binary observations in complex surveys
Author(s) -
So Hon Yiu,
Thompson Mary E.,
Wu Changbao
Publication year - 2020
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11551
Subject(s) - estimator , generalized estimating equation , binary data , binary number , gee , computer science , sampling (signal processing) , data mining , focus (optics) , statistics , survey data collection , econometrics , mathematics , machine learning , arithmetic , physics , filter (signal processing) , optics , computer vision
Misclassifications in binary responses have long been a common problem in medical and health surveys. One way to handle misclassifications in clustered or longitudinal data is to incorporate the misclassification model through the generalized estimating equation (GEE) approach. However, existing methods are developed under a non‐survey setting and cannot be used directly for complex survey data. We propose a pseudo‐GEE method for the analysis of binary survey responses with misclassifications. We focus on cluster sampling and develop analysis strategies for analyzing binary survey responses with different forms of additional information for the misclassification process. The proposed methodology has several attractive features, including simultaneous inferences for both the response model and the association parameters. Finite sample performance of the proposed estimators is evaluated through simulation studies and an application using a real dataset from the Canadian Longitudinal Study on Aging.