A Comprehensive Framework for Evaluating Time to Event Predictions using the Restricted Mean Survival Time

TL;DR

This paper introduces a comprehensive, model-agnostic framework for evaluating and interpreting restricted mean survival time (RMST) estimations in survival analysis, incorporating error estimation, prediction intervals, and variable importance assessments.

Contribution

It presents a novel, model-agnostic framework for evaluating RMST estimators, including error estimation, prediction intervals, and variable importance tests, applicable under model misspecification.

Findings

The framework accurately estimates mean squared error of RMST estimators.

It provides valid prediction intervals for RMST predictions.

The approach assesses variable importance both locally and globally.

Abstract

The restricted mean survival time (RMST) is a widely used quantity in survival analysis due to its straightforward interpretation. For instance, predicting the time to event based on patient attributes is of great interest when analyzing medical data. In this paper, we propose a novel framework for evaluating RMST estimations. A criterion that estimates the mean squared error of an RMST estimator using Inverse Probability Censoring Weighting (IPCW) is presented. A model-agnostic conformal algorithm adapted to right-censored data is also introduced to compute prediction intervals and to evaluate local variable importance. Finally, a model-agnostic statistical test is developed to assess global variable importance. Our framework is valid for any RMST estimator that is asymptotically convergent and works under model misspecification.