Work

Federated Time‐to‐Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD

Year: 2026

Type: article

Abstract: Time-to-event analyses on individual patient data are commonly performed with Cox or Poisson regression. The statistical power of epidemiological findings from such analyses is often sufficient when a large amount of data is analyzed, especially when studying rare outcomes or rare exposures. The increase of sample size can be achieved by co-analyzing data collected from different cohorts and hosted at different locations. For co-analyzing data located at different centers, the common options are either (i) to physically transfer and pool the data into a single dataset and analyze them jointly or (ii) to conduct a two-step meta-analysis where a separate analysis is done at each center and the center-specific results are then pooled and combined into a single estimate. DataSHIELD offers a third option where individual patient data from different locations are analyzed in a federated manner without transporting any data. In this paper, we describe how a federated Poisson regression can be conducted on time-to-event data using this infrastructure, providing relevant estimates.

Source: Statistics in Medicine

DOI: 10.1002/sim.70789

Authors: Bodil Svennblad, Benedicte Delcoigne, Paul Burton, Demetris Avraam

Institutions: Uppsala University, Karolinska Institutet, Newcastle University, University of Copenhagen

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,450