Document Detail


The Manton-Woodbury model for longitudinal data with dropouts.
MedLine Citation:
PMID:  9004397     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Often in longitudinal studies one is not able to obtain a complete set of measurements of the variable recorded over time for each person in the study. This could be caused by some of the persons dying (or leaving the study for some other reasons) while the study is going on. If there is any concern that such missing data (which have been termed dropouts) and the variables measured over time affect each other, a model for the joint distribution is needed. For a review of several such models see Hogan and Laird (in this volume). A model of the same kind was proposed by Woodbury and Manton and developed further later on. In this model it is possible to describe the evolution of the distribution of the variable measured over time when exposed to mortality selection. In contrast to other models, this allows for an explicit description of the interaction between the variable measured over time and the time to dropout. We describe the model and propose some generalizations. The theory is illustrated by some Monte Carlo simulations.
Authors:
T Martinussen; N Keiding
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
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:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  273-83     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, University of Copenhagen, Denmark.
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MeSH Terms
Descriptor/Qualifier:
Adult
Body Mass Index
Data Interpretation, Statistical
Humans
Longitudinal Studies*
Male
Models, Statistical*
Monte Carlo Method
Proportional Hazards Models
Stochastic Processes
Survival Analysis

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


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