People

Eric Polley, PhD

Associate Professor of Public Health Sciences
Research and Scholarly Interests: Assay Development, Cancer Genomics, Causal Inference, Data Science, Precision Medicine, Prognostic Risk Models, Risk Assessment
Websites: Biostatistics Laboratory, Google Scholar, Research Network Profile
Contact: epolley@uchicago.edu
Graduate Programs: Public Health Sciences, UChicago Biosciences

Director, Biostatistics Laboratory

Eric Polley, PhD is an Associate Professor in the Department of Public Health Sciences at The University of Chicago where he is the faculty director for the Data Science concentration in the Master of Public Health program. Dr. Polley was previously an Assistant Professor of Biostatistics in the Department of Quantitative Health Sciences at Mayo Clinic (2015-2021) and a mathematical statistician in the Biometric Research Branch at the U.S. National Cancer Institute (2012-2015). Dr. Polley received his PhD in biostatistics from the University of California, Berkeley in 2010. With Mark van der Laan, they developed the Super Learner ensemble prediction methodology. His research area involves the development and evaluation of prediction methods, innovative methods for diagnostic and prognostic prediction, and precision medicine clinical trial design.

National Cancer Institute
MD
Fellow - Computational Biology
2012

University of California, Berkeley
CA
PhD - Biostatistics
2010

Columbia University
NY
MS - Biostatistics
2005

Saint John's University
MN
BA - Mathematics
2003

Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource.
Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource. Mol Cancer Ther. 2023 Nov 01; 22(11):1270-1279.
PMID: 37550087

Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption.
Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption. Exp Clin Psychopharmacol. 2023 Oct 12.
PMID: 37824232

Electronic Health Records (EHRs) Can Identify Patients at High Risk of Fracture but Require Substantial Race Adjustments to Currently Available Fracture Risk Calculators.
Electronic Health Records (EHRs) Can Identify Patients at High Risk of Fracture but Require Substantial Race Adjustments to Currently Available Fracture Risk Calculators. J Gen Intern Med. 2023 Sep 15.
PMID: 37715097

Assessing the use of observational methods and real-world data to emulate ongoing randomized controlled trials.
Assessing the use of observational methods and real-world data to emulate ongoing randomized controlled trials. Clin Trials. 2023 Aug 17; 17407745231193137.
PMID: 37589143

Comparative effectiveness of second line glucose lowering drug treatments using real world data: emulation of a target trial.
Comparative effectiveness of second line glucose lowering drug treatments using real world data: emulation of a target trial. BMJ Med. 2023; 2(1):e000419.
PMID: 37577025

Functional and Clinical Characterization of Variants of Uncertain Significance Identifies a Hotspot for Inactivating Missense Variants in RAD51C.
Functional and Clinical Characterization of Variants of Uncertain Significance Identifies a Hotspot for Inactivating Missense Variants in RAD51C. Cancer Res. 2023 08 01; 83(15):2557-2571.
PMID: 37253112

Intermuscular Coherence in Spinocerebellar Ataxias 3 and 6: a Preliminary Study.
Intermuscular Coherence in Spinocerebellar Ataxias 3 and 6: a Preliminary Study. Cerebellum. 2023 Jul 10.
PMID: 37428409

Clot composition and recanalization outcomes in mechanical thrombectomy.
Clot composition and recanalization outcomes in mechanical thrombectomy. J Neurointerv Surg. 2023 Jul 07.
PMID: 37419694

Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival.
Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival. Cancer Med. 2023 08; 12(15):16142-16162.
PMID: 37401034

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.
Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method. Am Heart J. 2023 06; 260:124-140.
PMID: 36893934

View All Publications

NCI Director's award for the development of the NCI-MATCH clinical trial
National Cancer Institute
2015