Integration of survival data from multiple studies

Steffen Ventz, Rahul Mazumder, Lorenzo Trippa

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure 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. The estimation of the study-specific parameters utilizes 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 predictions compared to alternative meta-analytic methods.

Original languageEnglish (US)
Pages (from-to)1365-1376
Number of pages12
JournalBiometrics
Volume78
Issue number4
DOIs
StatePublished - Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
Steffen Ventz and Lorenzo Trippa were partially supported by NIH Grant 1R01LM013352‐01A1. Rahul Mazumder was partially supported by ONR Grants ONR‐N000141512342, ONR‐N000141812298, and NSF Grant NSF‐IIS‐1718258.

Publisher Copyright:
© 2021 The International Biometric Society.

Keywords

  • hierarchical regularization
  • meta-analysis
  • penalized regression
  • risk prediction
  • survival analysis

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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