Document Detail

Marginally specified generalized linear mixed models: a robust approach.
MedLine Citation:
PMID:  12495126     Owner:  NLM     Status:  MEDLINE    
Longitudinal data modeling is complicated by the necessity to deal appropriately with the correlation between observations made on the same individual. Building on an earlier nonrobust version proposed by Heagerty (1999, Biometrics 55, 688-698), our robust marginally specified generalized linear mixed model (ROBMS-GLMM) provides an effective method for dealing with such data. This model is one of the first to allow both population-averaged and individual-specific inference. As well, it adopts the flexibility and interpretability of generalized linear mixed models for introducing dependence but builds a regression structure for the marginal mean, allowing valid application with time-dependent (exogenous) and time-independent covariates. These new estimators are obtained as solutions of a robustified likelihood equation involving Huber's least favorable distribution and a collection of weights. Huber's least favorable distribution produces estimates that are resistant to certain deviations from the random effects distributional assumptions. Innovative weighting strategies enable the ROBMS-GLMM to perform well when faced with outlying observations both in the response and covariates. We illustrate the methodology with an analysis of a prospective longitudinal study of laryngoscopic endotracheal intubation, a skill that numerous health-care professionals are expected to acquire. The principal goal of our research is to achieve robust inference in longitudinal analyses.
J E Mills; C A Field; D J Dupuis
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Publication Detail:
Type:  Comment; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Biometrics     Volume:  58     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2002 Dec 
Date Detail:
Created Date:  2002-12-23     Completed Date:  2003-06-10     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  727-34     Citation Subset:  IM    
Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 3J5, Canada.
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MeSH Terms
Biometry / methods*
Data Interpretation, Statistical*
Intubation, Intratracheal* / methods,  standards
Likelihood Functions*
Linear Models*
Longitudinal Studies
Prospective Studies
Comment On:
Biometrics. 1999 Sep;55(3):688-98   [PMID:  11314994 ]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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