Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.

ABSTRACT:

Objective

The classification of complex or rare patterns in clinical and genomic data requires the availability of a large, labeled patient set. While methods that operate on large, centralized data sources have been extensively used, little attention has been paid to understanding whether models such as binary logistic regression (LR) can be developed in a distributed manner, allowing researchers to share models without necessarily sharing patient data.

Material and methods

Instead of bringing data to a central repository for computation, we bring computation to the data. The Grid Binary LOgistic REgression (GLORE) model integrates decomposable partial elements or non-privacy sensitive prediction values to obtain model coefficients, the variance-covariance matrix, the goodn

SUBMITTER: Wu Y 

PROVIDER: S-EPMC3422844 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature