Document Detail


Assessing interactions of binary time-dependent covariates with time in cox proportional hazards regression models using cubic spline functions.
MedLine Citation:
PMID:  8961465     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
H Heinzl; A Kaider; G Zlabinger
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  15     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  1996 Dec 
Date Detail:
Created Date:  1997-02-27     Completed Date:  1997-02-27     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  2589-601     Citation Subset:  IM    
Affiliation:
Department of Medical Computer Sciences, University of Vienna, Spitalgasse, Austria.
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MeSH Terms
Descriptor/Qualifier:
Clinical Trials as Topic / 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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