A Weighted Survival Regression Framework for Incorporating External Prediction Information

TL;DR

This paper introduces a new statistical method that uses external predictions to improve survival analysis of time-to-event data.

Contribution

A novel weighted survival regression framework that incorporates external prediction models without requiring their internal structure.

Findings

The proposed method can use any external prediction model and works with standard software.

New theoretical results and a perturbation-based inference method were developed to handle statistical challenges.

The approach was successfully applied to three public datasets.

Abstract

In this article, we develop a weighted approach to estimation for right-censored time to event data in the presence of external predictions available from a prediction model. There are several advantages to the proposed approach. First, the method allows for arbitrary forms for the external prediction model. Second, the methodology can be fit easily using standard software packages that allow for subject-specific weights. Third, all that is needed from the external models are access to predictions and not the actually prediction equation. A complication is that inference becomes challenging, so we develop new theoretical results along with a perturbation-based method for inference. The methodology is applied to three publicly available datasets.

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