Document Detail

Semiparametric models for missing covariate and response data in regression models.
MedLine Citation:
PMID:  16542244     Owner:  NLM     Status:  MEDLINE    
We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.
Qingxia Chen; Joseph G Ibrahim
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Biometrics     Volume:  62     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2006 Mar 
Date Detail:
Created Date:  2006-03-17     Completed Date:  2006-07-21     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  177-84     Citation Subset:  IM    
Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232, USA.
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MeSH Terms
Clinical Trials as Topic / statistics & numerical data*
Computer Simulation
Likelihood Functions
Linear Models*
Melanoma / therapy
Grant Support

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