A secure distributed logistic regression protocol for the detection of rare adverse drug events.

ABSTRACT:

Background

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak.

Objective

To develop a secure distributed multi-party computation protocol for logistic regression that

SUBMITTER: El Emam K 

PROVIDER: S-EPMC3628043 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature