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Emrah Gecili, PhD


  • Member, Division of Biostatistics and Epidemiology
  • Assistant Professor, UC Department of Pediatrics

About

Biography

I am an assistant professor in biostatistics and epidemiology at Cincinnati Children’s. I strive to build advanced prediction models incorporating clinical and demographic characteristics, omics and imaging as predictors of rapid disease progression. Over the years, I have gained experience and expertise in utilizing biostatistical and machine learning tools, developing new methods for problems of interest, and implementing computational algorithms for analyzing high-dimensional datasets that arise from different areas of applied science such as proteomics, genomics and medicine.

My research focuses on developing and validating biostatistical models incorporating biomarker data for outcomes prediction. Specifically, my interest lies in a variety of both Bayesian and traditional approaches for prediction and estimation models, including multiplicity adjustments for genomic data, as well as covariate modeling of proteomics, environmental exposures, and imaging as predictors of rapid human lung disease (such as in cystic fibrosis, lymphangioleiomyomatosis and bronchiolitis obliterans syndrome) progression. My research interest also includes building Bayesian regularized regression models for identifying potential predictive bio -/geo-markers for high dimensional regression problems.

I started working at Cincinnati Children’s as a postdoctoral fellow in 2018 and became an assistant professor in 2022. During my postdoctoral training, I was honored to be a two-year awardee of the Postdoctoral Fellowship from the US Cystic Fibrosis Foundation. My research is published in various journals, including Statistics in Medicine, Journal of Cystic Fibrosis, Chest, Science of the Total Environment, Journal of Clinical and Translational Science, Annals of the American Thoracic Society, Pediatric Pulmonology and Journal of Clinical Investigation Insight.

Postdoctoral Research Fellowship: Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 2022.

PhD: Statistics, University of Cincinnati, Cincinnati, OH, 2018.

MS: Statistics, University of Cincinnati, Cincinnati, OH, 2012.

Interests

Development and validation of biostatistical models incorporating biomarker data aimed at outcomes prediction; Bayesian and traditional approaches for prediction and estimation models; multiplicity adjustments for genomic data; covariate modeling of proteomics and imaging as predictors of rapid human lung disease progression; predicting employee injury risk.

Research Areas

Biostatistics and Epidemiology

Publications

Selected

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations. Szczesniak, R; Andrinopoulou, ER; Su, W; Afonso, PM; Burgel, PR; Cromwell, E; Gecili, E; Ghulam, E; Goss, CH; Mayer-Hamblett, N; Zhou, GC; Fernandez, G; Fink, AK; Schechter, MS. Annals of the American Thoracic Society. 2023; 20:958-968.

Selected

Lung function and secondhand smoke exposure among children with cystic fibrosis: A Bayesian meta-analysis. Reifenberg, J; Gecili, E; Pestian, T; Andrinopoulou, ER; Ryan, PH; Brokamp, C; Collaco, JM; Szczesniak, RD. Journal of Cystic Fibrosis. 2023; 22:694-701.

Selected

Predicting Individualized Lung Disease Progression in Treatment-Naive Patients With Lymphangioleiomyomatosis. Palipana, AK; Gecili, E; Song, S; Johnson, SR; Szczesniak, RD; Gupta, N. Chest. 2023; 163:1458-1470.

Selected

Built environment factors predictive of early rapid lung function decline in cystic fibrosis. Gecili, E; Brokamp, C; Rasnick, E; Afonso, PM; Andrinopoulou, ER; Dexheimer, JW; Clancy, JP; Keogh, RH; Ni, Y; Palipana, A; Su, W; Siracusa, C; Ryan, P; Szczesniak, RD. Pediatric Pulmonology. 2023; 58:1501-1513.

Selected

A default Bayesian multiple comparison of two binomial proportions. Gecili, E; Sivaganesan, S. Statistics and its Interface. 2023; 16:517-529.

Selected

Bayesian regularization for a nonstationary Gaussian linear mixed effects model. Gecili, E; Sivaganesan, S; Asar, O; Clancy, JP; Ziady, A; Szczesniak, RD. Statistics in Medicine. 2022; 41:681-697.

Selected

Sweat metabolomics before and after intravenous antibiotics for pulmonary exacerbation in people with cystic fibrosis. Woodley, FW; Gecili, E; Szczesniak, RD; Shrestha, CL; Nemastil, CJ; Kopp, BT; Hayes, D. Respiratory Medicine. 2022; 191:106687.

Selected

An empirical comparison of segmented and stochastic linear mixed effects models to estimate rapid disease progression in longitudinal biomarker studies. Su, W; Gecili, E; Wang, X; Szczesniak, RD. Statistics in Biopharmaceutical Research. 2021; 13:270-279.

Selected

Seasonal variation of lung function in cystic fibrosis: longitudinal modeling to compare a Midwest US cohort to international populations. Gecili, E; Brokamp, C; Palipana, A; Huang, R; Andrinopoulou, ER; Pestian, T; Rasnick, E; Keogh, RH; Ni, Y; Clancy, JP; Ryan, P; Szczesniak, RD. Science of the Total Environment. 2021; 776.