Work

A jackknife approach to estimate the prediction uncertainty from binary classifiers under right-censoring

Year: 2025

Type: article

Abstract: Clinical prediction models are developed to estimate a patient's risk for a specific outcome, and machine learning is frequently employed to improve prediction accuracy. When the outcome is some event that happens over time, binary classifiers can predict the risk at specific time points if right-censoring is addressed by inverse-probability-of-censoring-weighting . Assessing prediction uncertainty is crucial for interpreting individual risks, but there is limited knowledge on how to consider inverse-probability-of-censoring-weighting when estimating this uncertainty. We propose an adjustment of the infinitesimal jackknife estimator for the standard error of predictions that incorporates inverse-probability-of-censoring-weighting. By using a nonparametric approach, it is broadly applicable, especially to machine learning classifiers. For a simple tractable example, we show that the proposed adjustment reveals unbiased standard error estimates. For other situations, we evaluate performance through simulation studies under both parametric models with inverse-probability-of-censoring-weighting-customized log-likelihood and machine learning with inverse-probability-of-censoring-weighting-customized loss function. We illustrate the methods by predicting post-transplant survival probabilities, using national kidney transplant registry data. Our findings show that the proposed estimator is useful for quantifying prediction uncertainty of inverse-probability-of-censoring-weighting classifiers. Applications to simulated and real data show that prediction uncertainty increases when employing binary classifiers on dichotomized data compared to predictions from survival models.

Source: Statistical Methods in Medical Research

DOI: 10.1177/09622802251393626

Authors: Antje Jahn-Eimermacher, Lukas Klein, Gunter Grieser

Institutions: Darmstadt University of Applied Sciences, Martin Luther University Halle-Wittenberg

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,400