Open AccessLearning from local to global: An efficient distributed algorithm for modeling time-to-event data
Author(s) -
Rui Duan,
Chongliang Luo,
Martijn J. Schuemie,
Jiayi Tong,
Chen Liang,
Howard H. Chang,
Mary Regina Boland,
Jiang Bian,
Hua Xu,
John H. Holmes,
Christopher B. Forrest,
Sally C. Morton,
Jesse A. Berlin,
Jason H. Moore,
Kevin Mahoney,
Yong Chen
Publication year - 2020
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa044
Subject(s) - estimator , computer science , event (particle physics) , statistics , likelihood function , observational study , algorithm , missing data , data mining , econometrics , mathematics , estimation theory , quantum mechanics , physics
We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.