Calibration of prediction rules for life-time outcomes using prognostic Cox regression survival models and multiple imputations to account for missing predictor data with cross-validatory assessment

TL;DR

This paper extends calibration methods for survival prediction models, specifically Cox models, to handle censored data and missing predictors using multiple imputation and cross-validation, demonstrating improved predictive stability.

Contribution

It introduces a methodology combining imputation with predictive calibration for censored survival data, expanding prior binary outcome work to lifetime outcomes.

Findings

Prediction-averaging shows superior statistical properties.

Methods effectively handle missing data in calibration and validation.

Approaches improve predictive stability in survival models.

Abstract

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.