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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Generalized partially linear mixed-effects models incorporating mismeasured covariates
Annals of the Institute of Statistical Mathematics, v 61(1), pp 27-46
2009
PMID: 20160899
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this article we consider a semiparametric generalized mixed-effects model, and propose combining local linear regression, and penalized quasilikelihood and local quasilikelihood techniques to estimate both population and individual parameters and nonparametric curves. The proposed estimators take into account the local correlation structure of the longitudinal data. We establish normality for the estimators of the parameter and asymptotic expansion for the estimators of the nonparametric part. For practical implementation, we propose an appropriate algorithm. We also consider the measurement error problem in covariates in our model, and suggest a strategy for adjusting the effects of measurement errors. We apply the proposed models and methods to study the relation between virologic and immunologic responses in AIDS clinical trials, in which virologic response is classified into binary variables. A dataset from an AIDS clinical study is analyzed.
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Details
- Title
- Generalized partially linear mixed-effects models incorporating mismeasured covariates
- Creators
- Hua Liang - University of Rochester
- Publication Details
- Annals of the Institute of Statistical Mathematics, v 61(1), pp 27-46
- Publisher
- Springer-Verlag
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000263127800002
- Scopus ID
- 2-s2.0-59849118479
- Other Identifier
- 991019320713804721
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- Web of Science research areas
- Statistics & Probability