Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects | Zendy

Leena Choi | Zendy; Robert J. Carroll | Zendy; Cole Beck | Zendy; Jonathan D. Mosley | Zendy; Dan M. Roden | Zendy; Joshua C. Denny | Zendy; Sara L. Van Driest | Zendy

Open Access

Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects

Author(s) -

Leena Choi,

Robert J. Carroll,

Cole Beck,

Jonathan D. Mosley,

Dan M. Roden,

Joshua C. Denny,

Sara L. Van Driest

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty306

Subject(s) - leverage (statistics) , computer science , adverse effect , econometrics , machine learning , medicine , pharmacology , mathematics

Phenome-wide association studies (PheWAS) have been used to discover many genotype-phenotype relationships and have the potential to identify therapeutic and adverse drug outcomes using longitudinal data within electronic health records (EHRs). However, the statistical methods for PheWAS applied to longitudinal EHR medication data have not been established.

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