Model Fit Statistics

Suppose the model contains p estimable parameters. Then the following two criteria are displayed for model fit statistics:

  • –2 log likelihood:

    \[  -2\mbox{ Log L}=-2 \log (L(\hat{\bbeta }))  \]

    where $L(.)$ is a partial pseudo-likelihood function for the corresponding TIES= option as described in the section Partial Likelihood Function for the Cox Model, and $\hat{\bbeta }$ is the maximum pseudo-log-likelihood estimate of the proportional hazards regression coefficients.

  • Akaike’s information criterion (AIC):

    \[  \mbox{AIC}=-2\mbox{ Log L}+2p  \]

The AIC statistics gives a different way of adjusting the –2 log likelihood statistic for the number of estimable parameters in the model.