| Flexible survival regression modelling. | |
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MedLine Citation:
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PMID: 19608605 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Giuliana Cortese; Thomas H Scheike; Torben Martinussen |
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Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review Date: 2009-07-16 |
Journal Detail:
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Title: Statistical methods in medical research Volume: 19 ISSN: 1477-0334 ISO Abbreviation: Stat Methods Med Res Publication Date: 2010 Feb |
Date Detail:
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Created Date: 2010-02-08 Completed Date: 2010-04-20 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9212457 Medline TA: Stat Methods Med Res Country: England |
Other Details:
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Languages: eng Pagination: 5-28 Citation Subset: IM |
Affiliation:
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Department of Statistical Sciences, University of Padova, Padova, Italy. gcortese@stat.unipd.it |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Breast Neoplasms
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mortality Female Humans Norway / epidemiology Proportional Hazards Models Regression Analysis* Statistics, Nonparametric Survival Analysis* Time Factors |
| Grant Support | |
ID/Acronym/Agency:
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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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