Document Detail

Model-based approaches to analysing incomplete longitudinal and failure time data.
MedLine Citation:
PMID:  9004396     Owner:  NLM     Status:  MEDLINE    
Since Wu and Carroll (Biometrics 44, 175-188) proposed a model for longitudinal progression in the presence of informative dropout, several researchers have developed and studied models for situations where both a vector of repeated outcomes and an event time is available for each subject. These models have been developed for either longitudinal studies with dropout or for survival studies in which a random, time-varying covariate is measured repeatedly across time. When inference about the longitudinal variable is of interest, event times are treated as covariates and are often incomplete due to censoring. If survival or event time is the primary endpoint, repeated outcomes observed prior to the event are viewed as covariates; this covariate process is often incomplete, measured with error, or observed at unscheduled times during the study. We review several models which are used to handle incomplete response and covariate data in both survival and longitudinal studies.
J W Hogan; N M Laird
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Statistics in medicine     Volume:  16     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:    1997 Jan 15-Feb 15
Date Detail:
Created Date:  1997-03-26     Completed Date:  1997-03-26     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  259-72     Citation Subset:  IM    
Center for Statistical Sciences, Brown University, Providence, RI 02192, USA.
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MeSH Terms
Data Interpretation, Statistical
Likelihood Functions
Longitudinal Studies*
Models, Statistical*
Multivariate Analysis
Random Allocation
Regression Analysis
Survival Analysis
Grant Support

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