Document Detail


Flexible survival regression modelling.
MedLine Citation:
PMID:  19608605     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varying effects. We start by considering classical and also more recent goodness-of-fit procedures for the Cox model that will reveal when the Cox model does not capture important aspects of the data, such as time-varying effects. We present recent regression models that are able to deal with and describe such time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox's regression model and the need for other supplementary analyses with models such as those we present here.
Authors:
Giuliana Cortese; Thomas H Scheike; Torben Martinussen
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review     Date:  2009-07-16
Journal Detail:
Title:  Statistical methods in medical research     Volume:  19     ISSN:  1477-0334     ISO Abbreviation:  Stat Methods Med Res     Publication Date:  2010 Feb 
Date Detail:
Created Date:  2010-02-08     Completed Date:  2010-04-20     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9212457     Medline TA:  Stat Methods Med Res     Country:  England    
Other Details:
Languages:  eng     Pagination:  5-28     Citation Subset:  IM    
Affiliation:
Department of Statistical Sciences, University of Padova, Padova, Italy. gcortese@stat.unipd.it
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MeSH Terms
Descriptor/Qualifier:
Breast Neoplasms / mortality
Female
Humans
Norway / epidemiology
Proportional Hazards Models
Regression Analysis*
Statistics, Nonparametric
Survival Analysis*
Time Factors
Grant Support
ID/Acronym/Agency:
2 R01 CA54706-10/CA/NCI NIH HHS

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


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