Measures of predictive accuracy of regression models quantify the extent to which covariates determine an individual outcome. Schemper and Henderson’s (2000) proposed predictive accuracy measure is defined as the difference between individual processes and the fitted survivor function.
For the th individual (), let and be the left-truncation time, observed time, event indicator (1 for death and 0 for censored), and covariate vector, respectively. If there is no delay entry, then . Let be distinct event times with deaths at . The survival process for the th individual is
Let be the Kaplan-Meier estimate of the survivor function (assuming no covariates). Let be the fitted survivor function with covariates , and if you specify TIES=EFRON, then is computed by the Efron method; otherwise, the Breslow estimate is used.
The predictive accuracy is defined as the difference between individual survival processes and the fitted survivor functions with ()) or without () covariates between and , the largest observed time. For each death time , define a mean absolute distance between the and the as
where . Let be defined similarly to , but with replaced by and replaced by . Let be the Kaplan-Meier estimate of the censoring or potential follow-up distribution, and let
The overall estimator of the predictive accuracy with () and without () covariates are weighted averages of and , respectively, given by
The explained variation by the Cox regression is
Because the predictive accuracy measures and are based on differences between individual survival processes and fitted survivor functions, a smaller value indicates a better prediction. For this reason, and are also referred to as predictive inaccuracy measures.