Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates
Authors: Zhijian Chen, Grace Y. Yi, and Changbao Wu
Overview
Abstract (English)
Marginal methods have been widely used for the analysis of longitudinal ordinal and categorical data. These models do not require full parametric assumptions on the joint distribution of repeated response measurements but only specify the marginal or even association structures. However, inference results obtained from these methods often incur serious bias when variables are subject to error. In this paper, we tackle the problem that misclassification exists in both response and categorical covariate variables. We develop a marginal method for misclassification adjustment, which utilizes second-order estimating functions and a functional modeling approach, and can yield consistent estimates and valid inference for mean and association parameters. We propose a two-stage estimation approach for cases in which validation data are available. Our simulation studies show good performance of the proposed method under a variety of settings. Although the proposed method is phrased to data with a longitudinal design, it also applies to correlated data arising from clustered and family studies, in which association parameters may be of scientific interest. The proposed method is applied to analyze a dataset from the Framingham Heart Study as an illustration.
Abstract (French)
Please note that abstracts only appear in the language of the publication and might not have a translation.
Details
Type | Journal article |
---|---|
Author | Zhijian Chen, Grace Y. Yi, and Changbao Wu |
Publication Year | 2014 |
Title | Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates |
Volume | 56 |
Journal Name | Biometrical Journal |
Number | 1 |
Pages | 69-85 |
Publication Language | English |
- Zhijian Chen
- Zhijian Chen, Grace Y. Yi, and Changbao Wu
- Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates
- Biometrical Journal
- 56
- 2014
- 1
- 69-85