Document Detail

Analysis of longitudinal health-related quality of life data with terminal events.
MedLine Citation:
PMID:  16817007     Owner:  NLM     Status:  MEDLINE    
Longitudinal health-related quality of life data arise naturally from studies of progressive and neurodegenerative diseases. In such studies, patients' mental and physical conditions are measured over their follow-up periods and the resulting data are often complicated by subject-specific measurement times and possible terminal events associated with outcome variables. Motivated by the "Predictor's Cohort" study on patients with advanced Alzheimer disease, we propose in this paper a semiparametric modeling approach to longitudinal health-related quality of life data. It builds upon and extends some recent developments for longitudinal data with irregular observation times. The new approach handles possibly dependent terminal events. It allows one to examine time-dependent covariate effects on the evolution of outcome variable and to assess nonparametrically change of outcome measurement that is due to factors not incorporated in the covariates. The usual large-sample properties for parameter estimation are established. In particular, it is shown that relevant parameter estimators are asymptotically normal and the asymptotic variances can be estimated consistently by the simple plug-in method. A general procedure for testing a specific parametric form in the nonparametric component is also developed. Simulation studies show that the proposed approach performs well for practical settings. The method is applied to the motivating example.
Zhezhen Jin; Mengling Liu; Steven Albert; Zhiliang Ying
Related Documents :
2293747 - Matching and efficiency in cohort studies.
20172787 - Array-gain constraint minimum-norm spatial filter with recursively updated gram matrix ...
20879367 - Registration of longitudinal image sequences with implicit template and spatial-tempora...
12111887 - A bayesian space varying parameter model applied to estimating fertility schedules.
24297437 - Factor copula models for item response data.
15803227 - Disruptive visions: predictive simulation--between scientific method and clinical trial...
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.     Date:  2006-07-01
Journal Detail:
Title:  Lifetime data analysis     Volume:  12     ISSN:  1380-7870     ISO Abbreviation:  Lifetime Data Anal     Publication Date:  2006 Jun 
Date Detail:
Created Date:  2006-08-14     Completed Date:  2006-11-14     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9516348     Medline TA:  Lifetime Data Anal     Country:  United States    
Other Details:
Languages:  eng     Pagination:  169-90     Citation Subset:  IM    
Department of Biostatistics, Columbia University, New York, NY, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Health Status
Longitudinal Studies
Models, Statistical
Quality of Life*
Terminally Ill*
United States

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

Previous Document:  Dynamic path analysis-a new approach to analyzing time-dependent covariates.
Next Document:  Do provocateurs' emotion displays influence children's social goals and problem solving?