General Information
will be available soon...
Who are We
?
Topics
-
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.
Research
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
Thesis
will be available soon...
Projects
-
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)
Presentations
-
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.
Software
Our group is experienced in
Selected Literature
Survival:
-
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:
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.
|
