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Consequently, as lamented by van Houwelingen [ 12], the baseline survival function is almost never given by authors of published medical articles that report a Cox model.

A well-known non-parametric estimate of the baseline survival function is available [ 27], but it is impracticable to report the result concisely.

The baseline survival function is crucial, since it encapsulates the information needed to assess calibration of survival probabilities in the derivation dataset, and more importantly, calibration in validation datasets.

As we have shown, obtaining a simple but adequate approximation to the baseline survival function is not difficult, and indeed can be tackled in other ways if desired (e.g. spline functions [ 45]).

Similarly, the baseline survival function is calculated in the external dataset using both the parameter vector from this study cohort, together with the (log) time values in the external dataset and the set of spline knots used in the current cohort [ 34].

The corresponding survival function is written as (S 0 t) being the baseline survival function).

The survival function is estimated using the Kaplan-Meier estimator.

Its survival function is of the form (1).

The joint bivariate survival function is plotted in Fig. 5.

A Kaplan-Meier survival function is shown in Fig. 2.

Without the baseline survival function, it is not possible to judge how good the calibration in an independent sample is.