This paper is in the following e-collection/theme issue:

Security in Digital Health (20) Ethics, Privacy, and Legal Issues (393) Statistical Methods (23)

Published on in Vol 9 , No 1 (2021) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22158, first published .
A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

Authors of this article:

Marcel von Maltitz 1 Author Orcid Image ;   Hendrik Ballhausen 2, 3 Author Orcid Image ;   David Kaul 4, 5 Author Orcid Image ;   Daniel F Fleischmann 2, 3, 6 Author Orcid Image ;   Maximilian Niyazi 2, 3 Author Orcid Image ;   Claus Belka 2, 3 Author Orcid Image ;   Georg Carle 1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Wirth F, Kussel T, Müller A, Hamacher K, Prasser F. EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation. BMC Bioinformatics 2022;23(1) View