| Assessing interactions of binary time-dependent covariates with time in cox proportional hazards regression models using cubic spline functions. | |
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MedLine Citation:
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PMID: 8961465 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The Cox proportional hazards model is the most popular model for the analysis of survival data. Time-dependent covariates can be included in a straightforward manner. In most cases such covariates will be binary, indicating some form of changing group membership, with individuals starting in group 0, and changing into group 1 after the occurrence of a specific event. If there is evidence that the hazard ratio between these two groups depends on the sojourn time in group 1, then the use of cubic spline functions will allow investigation of the shape of the supposed effect and provide two main advantages-no particular functional form has to be specified and standard computer software packages like SAS or BMDP can be used. |
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Authors:
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H Heinzl; A Kaider; G Zlabinger |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Statistics in medicine Volume: 15 ISSN: 0277-6715 ISO Abbreviation: Stat Med Publication Date: 1996 Dec |
Date Detail:
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Created Date: 1997-02-27 Completed Date: 1997-02-27 Revised Date: 2007-11-15 |
Medline Journal Info:
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Nlm Unique ID: 8215016 Medline TA: Stat Med Country: ENGLAND |
Other Details:
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Languages: eng Pagination: 2589-601 Citation Subset: IM |
Affiliation:
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Department of Medical Computer Sciences, University of Vienna, Spitalgasse, Austria. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Clinical Trials as Topic
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methods* Data Interpretation, Statistical Humans Infection / drug therapy, etiology Kidney Transplantation / adverse effects, mortality Proportional Hazards Models* Regression Analysis* Risk Assessment Software Design Survival Analysis* Time Factors |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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