Marginal methods for correlated binary data with misclassified responses
Authors: Zhijian Chen, Grace Y. Yi, and Changbao Wu
Overview
Abstract (English)
Misclassification is a longstanding concern in medical research. Although there has been much research concerning error-prone covariates, relatively little work has been directed to problems with response variables subject to error. In this paper we focus on misclassification in clustered or longitudinal outcomes. We propose marginal analysis methods to handle binary responses which are subject to misclassification. The proposed methods have several appealing features, including simultaneous inference for both marginal mean and association parameters, and they can handle misclassified responses for a number of practical scenarios, such as the case with a validation subsample or replicates. Furthermore, the proposed methods are robust to model misspecification in a sense that no full distributional assumptions are required. Numerical studies demonstrate satisfactory performance of the proposed methods under a variety of settings.
Abstract (French)
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Details
Type | Journal article |
---|---|
Author | Zhijian Chen, Grace Y. Yi, and Changbao Wu |
Publication Year | 2011 |
Title | Marginal methods for correlated binary data with misclassified responses |
Volume | 98 |
Journal Name | Biometrika |
Number | 3 |
Pages | 647-662 |
Publication Language | English |
- Zhijian Chen
- Zhijian Chen, Grace Y. Yi, and Changbao Wu
- Marginal methods for correlated binary data with misclassified responses
- Biometrika
- 98
- 2011
- 3
- 647-662