title:         Covariate Functional Form in Cox Models
authors:       Luke Keele
entrydate:     2005-10-25 05:45:02
keywords:      Cox model, event history, survival models,
splines, semi-parametric, duration models
abstract:      In most event history models, the effect of a covariate
on the hazard is assumed to have a log-linear functional form. For
continuous covariates, this assumption is often violated as the
effect is highly nonlinear.  Assuming a log-linear functional form
when the nonlinear form applies causes specification errors
leading to erroneous statistical conclusions. Scholars can,
instead of ignoring the presence of nonlinear effects, test for
such nonlinearity and incorporate it into the model.  I review
methods to test for and model nonlinear functional forms for
covariates in the Cox model.  Testing for such nonlinear effects
is important since such nonlinearity can appear as nonproportional
hazards, but time varying terms will not correct the
misspecification. I investigate the consequences of nonlinear
function forms using data on international conflicts from
1950-1985.  I demonstrate that the conclusions drawn from this
data depend on fitting the correct functional form for the
covariates.

http://polmeth.wustl.edu/retrieve.php?id=567

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