Document Detail

Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data.
MedLine Citation:
PMID:  23047604     Owner:  NLM     Status:  MEDLINE    
Longitudinal data arising from routine follow-up of patients will often have irregular measurement times. Existing methods for analysis include joint modelling of the outcome and measurement processes, and inverse-intensity weighting (IIW). This work extends previously proposed analysis of increments to the case of irregular follow-up, yielding a model for the increments that can be used as a stand-alone method. Furthermore, we propose two ways of combining the increments and IIW estimators. First, we use the increment model to select the truncation point for the inverse-intensity weights that minimises the mean squared error of the IIW estimator. Second, we use the increment model to augment the usual IIW estimating equations to form a doubly robust estimator. We evaluate the methods through simulation and apply these to a recent study of juvenile dermatomyositis.
Eleanor M Pullenayegum; Brian M Feldman
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Publication Detail:
Type:  Journal Article     Date:  2012-10-10
Journal Detail:
Title:  Statistics in medicine     Volume:  32     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2013 Mar 
Date Detail:
Created Date:  2013-02-15     Completed Date:  2013-08-20     Revised Date:  2014-02-20    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  1054-72     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 John Wiley & Sons, Ltd.
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MeSH Terms
Area Under Curve
Computer Simulation
Data Interpretation, Statistical*
Dermatomyositis / pathology
Follow-Up Studies
Longitudinal Studies*
Models, Statistical*
Stochastic Processes
Erratum In:
Stat Med. 2014 Feb 10;33(3):540

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

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