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Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions
Author(s) -
Hess Kenneth R.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780131007
Subject(s) - covariate , proportional hazards model , statistics , regression analysis , mathematics , regression , spline (mechanical) , econometrics , engineering , structural engineering
Proportional hazards (or Cox) regression is a popular method for modelling the effects of prognostic factors on survival. Use of cubic spline functions to model time‐by‐covariate interactions in Cox regression allows investigation of the shape of a possible covariate‐time dependence without having to specify a specific functional form. Cubic spline functions allow one to graph such time‐by‐covariate interactions, to test formally for the proportional hazards assumption, and also to test for non‐linearity of the time‐by‐covariate interaction. The functions can be fitted with existing software using relatively few parameters; the regression coefficients are estimated using standard maximum likelihood methods.