Hakan Demirtas

Associate Professor
Biostatistics

Contact Information

E-mail: demirtas@uic.edu

Address:
950 Sphpi, M/c 923, M/C 923
1603 W. Taylor St
Chicago, IL 60612

Phone: 312-996-9841

Academic Background

PHD, Statistics, Penn State U Main Campus, 2003
MBA, Business Administration (MBA), Univ of South Alabama, 1997

Research Interests

Weighted estimating equations, multiple imputation, statistical computing, techniques for nonignorable missingness, analysis of incomplete multivariate data, analysis of longitudinal data, Bayesian computing, random number generation, generalized linear and nonlinear mixed models.

Teaching Interests

Computational statistics and Bayesian data analysis.

Publications

Wang, X., Qin X., Demirtas, H., Li, J., Mao, G., Huo, Y., Sun, N., Liu, L. & Xu, X. (2007). Effect of folic acid supplementation on risk of stroke. Forthcoming in Lancet.

Demirtas, H. & Hedeker, D. (2007). Gaussianization-based quasi-imputation and expansion strategies for correlated binary responses. Statistics in Medicine, 26, 782-799.

Wang, X., Demirtas, H. & Xu, X. (2006). Homocysteine, B vitamins, and cardiovascular diesase. New England Journal of Medicine, Volume 355, Issue 2, 207-209.

Dorevitch, S., Demirtas, H., Scheff, P.A. & Persky, V.W. (2007). Bias and Confounding in Longitudinal Measures of Exhaled Monoxides. Forthcoming in Journal of Exposure Science and Environmental Epidemiology.

Dorevitch, S., Demirtas, H., Persky, V.W, Erdal, S., Conroy, L., Schoonover, T. & Scheff, P.A. (2006). Demolition of high-rise public housing increases particulate matter air pollution in communities of high-risk asthmatics. Journal of the Air and Waste Management Association, Volume 56, 1022-1032.

Demirtas, H., Freels, S.A. & Yucel, R.M. (2007). Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. Forthcoming in Journal of Statistical Computation and Simulation.

Demirtas, H. & Hedeker, D. (2006). Comment on “Tukey’s gh distribution for multiple imputation”. American Statistician, Volume 60, No 4, 348-349.

Demirtas, H. (2007). Practical advice on how to impute continuous data when the ultimate interest centers on dichotomized outcomes through pre-specified thresholds. Forthcoming on Issue 4 of 2007 in Communications in Statistics-Simulation and Computation.

Demirtas, H. (2004). Simulation-driven inferences for multiply imputed longitudinal datasets. Statistica Neerlandica. Volume 58, Issue 4, 466-482.

Demirtas, H. (2006). A method for multivariate ordinal data generation given marginal distributions and correlations. Journal of Statistical Computation and Simulation, Volume 76, Number 11, 1017-1025.

Demirtas, H. (2004). Modeling incomplete longitudinal data. Journal of Modern Applied Statistical Methods, Volume 3, No 2, 305-321.

Dorevitch, S., Tharenos, L., Persky, V.W., Demirtas, H., Artwohl, L. & Fortman, J.D. (2007). Rural environment in infancy protects against sensitization to rodents in adulthood. Forthcoming in Annals of Allergy, Asthma and Immunology.

Wang, X., Qin X., Demirtas, H., Li, J., Mao, G., Huo, Y., Sun, N., Liu, L. & Xu, X. (2007). Efficacy of folic acid supplementation in stroke prevention: a meta-analysis. Lancet, Volume 369, 1876-1882.

Demirtas, H. (2007). The design of simulation studies in medical statistics. Statistics in Medicine, 26, 3818-3821.

Demirtas, H. (2007). Practical advice on how to impute continuous data when the ultimate interest centers on dichotomized outcomes through pre-specified thresholds. Communications in Statistics-Simulation and Computation, Volume 36, Issue 4, 871-889.

Demirtas, H., Arguelles, L.M., Chung, H. & Hedeker, D. (2007). On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation. Computational Statistics and Data Analysis, Volume 51, Issue 8, 4064-4068.

Demirtas, H., Freels, S.A. & Yucel, R.M. (2008). Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. Journal of Statistical Computation and Simulation, Volume 78, Issue 1, 69-84.

Hill, P.D., Aldag, J.C., Demirtas, H., Zinaman, M. & Chatterton, R.T. (2006). Mood states and milk output in lactating mothers of preterm and term infants. Journal of Human Lactation, 22(3), 305-314.

Demirtas, H., Arguelles, L.M., Chung, H. & Hedeker, D. (2007). On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation. Computational Statistics and Data Analysis, Volume 51, Issue 8, 4064-4068.

Demirtas, H. (2004). Modeling incomplete longitudinal data. Journal of Modern Applied Statistical Methods, Volume 3, No 2, 305-321.

Demirtas, H. & Hedeker, D. (2008). Imputing continuous data under some non-Gaussian distributions. Statistica Neerlandica, Volume 62, Issue 2, 193-205.

Hedeker, D., Mermelstein, R. & Demirtas, H. (2007). Analysis of binary outcomes with missing data: missing=smoking, last observation carried forward, and a little multiple imputation. Addiction, Volume 102, 1564-1573.

Doganay, S., Hepsen, I.F., Yologlu, S. & Demirtas, H. (2007). Effect of the preservation-to-surgery interval on corneal allograft survival in low-risk patients. Ophthalmic Surgery, Lasers and Imaging, Volume 38, No 6, 457-461.

Demirtas, H. (2008). On imputing continuous data when the eventual interest pertains to ordinalized outcomes via threshold concept. Computational Statistics and Data Analysis, Volume 52, Issue 4, 2261-2271.

Demirtas, H. & Schafer, J.L. (2003). On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 22, 2553-2575.

Demirtas, H. (2004). Simulation-driven inferences for multiply imputed longitudinal datasets. Statistica Neerlandica, Volume 58, Issue 4, 466-482.

Demirtas, H. (2005). Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 24, 2345-2363.

Demirtas, H. (2004). Pseudo-random number generation in R for commonly used multivariate distributions. Journal of Modern Applied Statistical Methods, Volume 3, No 2, 485-497.

Leon, A.C., Demirtas, H. & Hedeker, D. (2007). Bias reduction with an adjustment for participants’ intent to dropout of a randomized controlled clinical trial. Clinical Trials, Volume 4, No 5, 540-547.

Demirtas, H. (in press). A Method for Multivariate Ordinal Data Generation Given Marginal Distributions and Correlations. Journal of Statistical Computation and Simulation.

Demirtas, H. (2005). Pseudo-random number generation in R for some univariate distributions. Journal of Modern Applied Statistical Methods, Volume 4, No 1, 300-311.

Demirtas, H. (2005). Bayesian analysis of hierarchical pattern-mixture models for clinical trials data with attrition and comparisons to commonly used ad-hoc and model-based approaches. Journal of Biopharmaceutical Statistics, Volume 15, Issue 3, 383-402.

Demirtas, H. (2005). Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 24, 2345-2363.

Batcioglu, K., Ozturk, I.C., Atalay, S., Dogan, D., Bayri, N. & Demirtas, H. (2002). Investigation of time dependent magnetic field effect on superoxide dismutase and catalase activity: An in-vitro study. Journal of Biological Physics and Chemistry, 2, 108-112.

Demirtas, H. & Schafer, J.L. (2003). On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 22, 2553-2575.

Demirtas, H. (2004). Assessment of relative improvement due to weights within generalized estimating equations framework for incomplete clinical trials data. Journal of Biopharmaceutical Statistics. Volume 14, Issue 4, 1085-1098.

Demirtas, H. (2004). Assessment of relative improvement due to weights within generalized estimating equations framework for incomplete clinical trials data. Journal of Biopharmaceutical Statistics, Volume 14, Issue 4, 1085-1098.

Demirtas, H. & Hedeker, D. (2008). Multiple imputation under power polynomials. Forthcoming in Communications in Statistics-Simulation and Computation.