Journal article
Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data
Computational statistics & data analysis, Vol.51(12), pp.5776-5790
2007
DOI: 10.1016/j.csda.2006.10.008
PMCID: PMC2000853
PMID: 18704154
Abstract
Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.
Details
- Title: Subtitle
- Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data
- Creators
- Erning Li - Texas A&M UniversityDaowen Zhang - North Carolina State UniversityMarie Davidian - North Carolina State University
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.51(12), pp.5776-5790
- DOI
- 10.1016/j.csda.2006.10.008
- PMID
- 18704154
- PMCID
- PMC2000853
- NLM abbreviation
- Comput Stat Data Anal
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 2007
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257631302771
Metrics
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