Statistical analysis-meta-analysis/reproducibility

Mackenzie J. Edmondson, Chongliang Luo, Yong Chen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Federated learning has gained great popularities in the last decade for its capability of collaboratively building models on data from multiple datasets. However, in real-world biomedical settings, practical challenges remain, including the needs to protect privacy of the patients, the capability of accounting for between-site heterogeneity in patient characteristics, and, from operational point of view, the number of needed communications across data partners. In this chapter, we describe and provide examples of multi-database data-sharing mechanisms in the healthcare data context and highlight the primary methods available for performing statistical regression analysis in each setting. For each method, we discuss the advantages and disadvantages in terms of data privacy, data communication efficiency, heterogeneity awareness, and statistical accuracy. Our goal is to provide researchers with the insight necessary to choose among the available algorithms for a given setting of conducting regression analysis using multi-site data.

Original languageEnglish
Title of host publicationClinical Applications of Artificial Intelligence in Real-World Data
PublisherSpringer International Publishing
Pages125-139
Number of pages15
ISBN (Electronic)9783031366789
ISBN (Print)9783031366772
DOIs
StatePublished - Nov 4 2023

Fingerprint

Dive into the research topics of 'Statistical analysis-meta-analysis/reproducibility'. Together they form a unique fingerprint.

Cite this