Robust Updating of a Risk Prediction Model by Integrating External Ranking Information

TL;DR

This paper introduces a novel method for updating risk prediction models by integrating external ranking information, focusing on risk rankings rather than scores, to improve predictive accuracy across different study populations.

Contribution

The proposed approach leverages external risk rankings to enhance internal risk model estimation without requiring specific external model forms, improving transportability.

Findings

Method performs well with high rank correlation between models

Simulation studies show competitive predictive performance

Applied to prostate cancer, demonstrating practical utility

Abstract

Utilizing established risk factors and prognostic models can often improve the construction of a newer risk model that uses novel biomarkers in a smaller, internal study. However, directly borrowing information from an established prognostic model is often unsuitable due to differences in study populations, patient outcomes measured, and other specific features of the internal study design. To better enable the use of established prognostic information when constructing a novel risk model, we propose an estimation approach centered around the idea that the risk rankings rather than the risk scores from an established prognostic model are often more transportable to the internal study context. To leverage external ranking information, our approach introduces the ranking parameters associated with the regression coefficients of an internal risk model and estimates the internal risk model parameters by penalizing a ranking-based discrepancy measure between the ranking parameters and the rankings implied by the established prognostic model. Our method does not require the external prognostic model to have a specific form, but only requires one to compute risk score rankings from an external model. Simulation studies demonstrate that our method leads to competitive predictive performance and performs particularly well when the true internal and external prognostic models have high rank correlation but large discrepancies between their underlying risk scores. We demonstrate the use of our approach through the development of a prognostic model for advanced prostate cancer patients who were treated with an immune checkpoint inhibitor