Contact Us

Department of Statistics

Middle East Technical University

06531, Ankara / Turkey

E-mail : wwwbstat@metu.edu.tr

Phone :00 90 312 210 5305/5326


General Information

will be available soon...


Who are We ?



  • Survival Analysis

  • Longitudinal Data Analysis

  • Generalized Linear Models

  • Generalized Additive Models

  • Case-Control Data

  • Multiple Hypothesis Testing

  • Bayesian Approaches in Biostatistics

  • Genetic Association Studies

  • Microarray Analysis

  • Missing Data


Courses Offered

The undergraduate and graduate courses offered by our research group are listed below:

  • STAT 462 Biostatistics:                                                                                                                            Populations and samples. Types of biological data. Data transformations. Survival data analysis. Life tables. Sample size determination in clinical trials. Measures of association. The odds ratio and some properties. Application of generalized linear models and logistic regression to biological data. Analysis of data from matched samples.                                                                                                                     Prerequisite: STAT 156                 

  • STAT 482 Categorical Data Analysis:                                                                                                        Probability distributions and measures of association for count data. Inferences for two-way contingency tables. Generalized linear models, logistic regression and log-linear models. Models with fixed and random effects for categorical data. Model selection and diagnostics when response is categorical. Classification trees.                                                                                                                                    Prerequisite: STAT 272

  • STAT 560 Logistic Regression Analysis:                                                                                                    Introduction to categorical response data. Fitting logistic regression models. Interpretation of coefficients. Maximum likelihood estimation. Hypothesis testing. Model building and diagnostics. Polytomous logistic regression. Interaction and confounding. Logistic regression modeling for different sampling designs: case-control and cohort studies, complex surveys. Conditional logistic regression. Exact methods for small samples. Power and sample size. Recent developments in logistic regression approach.

  • STAT 561 Panel Data Analysis:                                                                                                                Introduction to longitudinal / panel data. Missing cases in panel data. Exploratory longitudinal data analysis. Marginal models, transition models, random effects models, multilevel (hierarchical) models. Estimation methods for this type of data.

  • STAT 567 Biostatistics and Statistical Genetics:                                                                                       Introduction to use of statistical methodology in health related sciences. Types of health data. Odds ratio, relative risk. Prospective and retrospective study designs. Cohort, case-control, matching case-control, case-cohort, nested case-control studies. Analysis of survival data. Kaplan-Meier, life tables, Cox’s proportional hazards model. Analysis of case-control data. Unconditional, conditional, polytomous logistic regression. Introduction to genetic epidemiology. Testing Hardy-Weinberg law. Linkeage analysis. Analysis of microarray data.  Association studies. Sample size and power. Recent developments in biostatistics and genetic epidemiology.

Graduate students from the following departments attended these courses so far:

  • Department of Statistics, METU

  • Department of Biological Sciences, METU

  • Bioinformatics Institute, METU

  • Department of Molecular Biology and Genetics, Bilkent University

  • Department of Economics, METU


Supplementary courses: These courses are offered in our department as must courses. Please check out the department’s homepage for further information on the topics covered in these courses.

  • STAT 457 Statistical Design of Experiments:                                                                                             This course is especially recommended for undergraduate/graduate students of biological sciences. It helps with designing the laboratory experiments and determining/applying the appropriate statistical method for the analysis of data coming from the designed experiments.

  • STAT 460 Nonparametric Statistics:                                                                                                         This course is useful for analyzing the data of the small scale laboratory experiments. The statistical methods covered in this course are used when the sample size is small.

  • STAT 363 Linear Models:                                                                                                                         Simple and Multiple Linear Regression Models.  Estimation ,  interval estimation and test of hypothesis on the parameters of the models. Model Adequecy Checking. Multicollinearity. Transformation.
    Prerequisite: MATH 260, STAT 156

  • STAT 356 Statistical Data Analysis:                                                                                                          This course provides introduction on many topics such as the presentation of data, categorical data analysis, and handling missing data. It mainly focuses on discussions with case studies.

  • STAT 361 Computational Statistics:                                                                                                          This course provides background and application on computational tools, such as random number generation, bootstrap, resampling, simulation and Markov Chain Monte Carlo methods.


Some recent research interests are:

  • Semiparametric Bayesian analysis of generalized linear models with nonignorably missing covariates.

  • Two stage regression approach for mixed effects model with clustered data.

  • Multilevel models for longitudinal binary data

  • Genome-wide association studies for small number of mother-father-child trios



will be available soon...



  • TÜBİTAK 1002, Küçük hücre dışı akciğer kanserinde 1-karbon yolağı enzim genotiplerinin potansiyel biyogöstergesi olarak Kanser-Testis gen ifadesinin önemi (statistical researcher)

  • FB7 European Research Comission, Long and short term health effects of GMO food (statistical researcher)

  • TÜBİTAK 1001, Nikotin ve Levamisol'ün Kanser Hücrelerinin Moleküler Fenotipi ve İfade Profilleri Üzerindeki Etkilerinin Karşılaştırmalı İşlevsel Genomik Metodlarla Araştırılması (statistical researcher)

  • TÜBİTAK 1001, Meme Kanserinde Epigenetik Olarak Susturulmuş Genlerin Yeniden Aktive Edilerek Gen İfade Profillerinin Belirlenmesi ve Özgün Hedef Genlerin Analizi (statistical researcher)

Please follow the links in Who are we? for a full description of the projects


Short Courses

  • Markov chain Monte Carlo methods for Analysis of Logistic Regression via WinBUGS (2009): Department of Statistics, Technische Universitat Dortmund. (Zeynep Kalaylıoğlu)

  • Introduction to Statistical Analysis of Microarray Data with R (2009 & 2010): METU, Central Laboratory, Molecular Biology and Biotechnology R&D Center (Özlem İlk)

  • Panel Veri Analizi (2009): TUİK (Özlem İlk)


  • The role of statistics in genetic association studies: an overview and update. Workshop in Recent Developments in Applied Probability and Statistics, Institute of Applied Mathematics, METU, Ankara, March 2009. (Zeynep Kalaylıoğlu)

  • Example of collaboration of health scientists and biostatisticians for a successful biomedical research: Hepatitis C virus infection and liver disease. Department of Statistics, METU, Ankara, 2008. (Zeynep Kalaylıoğlu)

  • Collaboration of health scientists and biostatisticians for a successful biomedical research: Two examples from National Institutes of Health-USA. Department of Biological Sciences, METU, Ankara, 2007. (Zeynep Kalaylıoğlu)

  • A two-stage logistic regression application: predictors of breast cancer risk by tumor size, grade, and nodal status. Graduate Summer School on New Advances in Statistics, Department of Statistics, METU, Ankara, 2007. (Zeynep Kalaylıoğlu)

  • Exploiting gene-environment independence in family-based case-control studies. Department of Statistics, METU, Ankara, February 2006. (Zeynep Kalaylıoğlu)

  • Introduction to Longitudinal Data Analysis, Ankara University & Institute of Applied Mathematics, METU, Ankara, 2010. (Özlem İlk)

  • Exploratory Data Analysis and Models for Multivariate Longitudinal Data, 4th EMR-IBS Conference, Israel, 2007. (Özlem İlk)

  • Panel Data Analysis, Graduate Summer School on New Advances in Statistics, Department of Statistics, METU, Ankara, 2007. (Özlem İlk)

Please follow the links in Who are we? for a full description of the presentations.



Our group is experienced in


Selected Literature


  • M. Tableman, J. S. Kim, Survival analysis using S : analysis of time-to-event data, Chapman and Hall, Boca Raton (2002)

  • D. G. Kleinbaum, M. Klein, Survival analysis: A self learning text, Springer, New York (2005)

Logistic Regression:

  • D. G. Kleinbaum, M. Klein, Logistic regression: A self learning text, Springer, Berlin (2010)

  • D. W. Hosmer, S. Lemeshow, Applied logistic regression, Wiley, New York (2000)

Modeling :

  • Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W., (1996) Applied Linear Statistical Models, 4th edition, Irwin.

Multiple Hypothesis Testing:

  • S. Dudoit, J.P. Shaffer, J.C. Boldrick, Multiple Hypothesis Testing in Microarray Experiments, Statist. Sci. Volume 18, Issue 1 (2003), 71-103.

  • S. Dudoit , M.J. Van Der Laan, Multiple testing procedures and applications to genomics, Springer, New York (2007)

Missing Data:

  • M. T. Tan, G.L. Tian, K.W. Ng, Bayesian missing data problems : EM, data augmentation and noniterative computation, Chapman and Hall, Boca Raton (2009)

Genetic Association/ Genetic Epidemiology:

  • D.C. Thomas Statistical Methods in Genetic Epidemiology, Oxford, New York (2004)

Categorical Data Analysis:

  • Agresti, A., (2002) Categorical Data Analysis, 2nd edition, Wiley, New York.

Case Studies:

  • Chatfield, C., (1980) Problem Solving: A Statistician’s Guide, 2nd edition, Chapman & Hall.

Longitudinal Data Analysis:

  • Diggle, P.J., Heagerty P., Liang K-Y, and Zeger, S.L. (2002) Analysis of Longitudinal Data, second edition. Oxford University

  • Weiss, R.E. (2005) Modeling Longitudinal Data. Springer, NY.



                                              Last Updated : 20 June 2011 - 15:00 (UTC +02:00)