A-calibration: assessment of prediction models for survival data under censoring.

ABSTRACT:

Background

Evaluating the performance of predictive models for survival is essential before they can be trusted for real-world applications and decision making. While good measures such as the C-index are available for model discrimination, the toolbox for model calibration is much more limited in the time-to-event setting. The method of D-calibration was therefore an important contribution that yields a single numeric value for calibration across the available follow-up time. D-calibration consists of performing a Pearson's goodness-of-fit test on transformed survival times. Censored survival times are handled using an imputation approach which however tends to yield a conservative test and loss of power.

Methods

In this paper, we introduce A-calibration based on Akritas's

SUBMITTER: Simonsen MR 

PROVIDER: S-EPMC12542389 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature