Integration of Survival Data from Multiple Studies

TL;DR

This paper presents a novel statistical method that integrates survival data from multiple studies, accounting for study differences, to improve survival prediction accuracy using hierarchical regularization and similarity matrices.

Contribution

The paper introduces a new hierarchical regularization approach that explicitly models study differences and borrows information across studies for better survival prediction.

Findings

The method improves prediction accuracy over existing meta-analytic techniques.

Simulation studies demonstrate the effectiveness of the approach.

Application to ovarian cancer datasets shows practical utility.

Abstract

We introduce a statistical procedure that integrates survival data from multiple biomedical studies, to improve the accuracy of predictions of survival or other events, based on individual clinical and genomic profiles, compared to models developed leveraging only a single study or meta-analytic methods. The method accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. Shrinkage of the study-specific parameters is controlled by a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene-expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival prediction compared to alternative meta-analytic methods.