Collinearity in a Cox regression leads to the same problems as it does in other forms of regression (unstable parameter estimates, difficulty in interpretation – see Section 5.19), and the solutions to the problem are the same (e.g., remove redundant variables). However, unlike
car::vif() function will not work with a
coxph object. Instead, first fit a linear regression model with any numeric variable as the outcome (here we use the event time variable) and compute the VIFs for that model.
<- lm(gestage37 ~ RF_PPTERM + MAGER + MRACEHISP + DMAR, pretend.lm data = natality.complete) ::vif(pretend.lm)car
## GVIF Df GVIF^(1/(2*Df)) ## RF_PPTERM 1.006 1 1.003 ## MAGER 1.162 1 1.078 ## MRACEHISP 1.146 3 1.023 ## DMAR 1.299 1 1.140