Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions
- 30 May 1994
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 13 (10) , 1045-1062
- https://doi.org/10.1002/sim.4780131007
Abstract
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.Keywords
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