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Activity Number: 100 - Optimizing Medical Decision Making with Real World Evidence
Type: Invited
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #326627 Presentation
Title: Conquering Massive Clinical Models with GPU Parallelized Logistic Regression
Author(s): Yuxi Tian* and Trevor Shaddox and Marc Suchard
Companies: UCLA and UCLA and UCLA
Keywords: Observational Studies; Large-Scale Models; Logistic Regression; GPU; Parallelization

Electronic, longitudinal health databases provide a wealth of real-world clinical data that enable very large observational studies. Logistic regression is a staple model for predicting binary outcomes in a variety of clinical study designs. However, there are inadequate computational tools in many situations involving large models. For example, propensity score estimation with logistic regression using thousands of covariates often involves statistical regularization that requires expensive cross-validation. Also, predicting outcomes in stratified data using conditional logistic regression to avoid nuisance parameter estimation becomes computationally infeasible for even moderately large strata. We present efficient and GPU parallelized implementations of conditional and unconditional logistic regression that allow for extensively cross-validated models with many thousands of predictors. We compare our methods to existing software packages and also propose extensions to other commonly used generalized linear models. We aim to remove computational burden as a barrier to using desired methods for massive problems in observational health research.

Authors who are presenting talks have a * after their name.

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