  In this paper, we expand the methodology presented in Mertens et. al (2020,
Biometrical Journal) to the study of life-time (survival) outcome which is
subject to censoring and when imputation is used to account for missing values.
We consider the problem where missing values can occur in both the calibration
data as well as newly - to-be-predicted - observations (validation). We focus
on the Cox model. Methods are described to combine imputation with predictive
calibration in survival modeling subject to censoring. Application to
cross-validation is discussed. We demonstrate how conclusions broadly confirm
the first paper which restricted to the study of binary outcomes only.
Specifically prediction-averaging appears to have superior statistical
properties, especially smaller predictive variation, as opposed to a direct
application of Rubin's rules. Distinct methods for dealing with the baseline
hazards are discussed when using Rubin's rules-based approaches.
