# MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS. Biostatistics

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1 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: Contingency Tables and Log Linear Models Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish Lecture BİS 510 ECTS Credit Contingency table statistics and multiway Chi- square analysis, risk analysis, correspondence statistics, Roc curve, sensitivity and reliability analysis, logistic regression. Be familiar with the statistics of contingency tables. At the end of the lecture students can analyze categorical data. Agresti, A., Categorical Data Analysis, John Willey & Sons., 1990 Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Variables in contingency tables Variables in contingency tables Relationship statistics between two or more statistics. Relationship statistics between two or more statistics. Multiway chi square statistics.

2 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Risk analysis Risk analysis Correspondence statistics Correspondence statistics Sensitivity and reliability coefficients Sensitivity and reliability coefficients Roc Curve Binary variables and logistic regression Binary variables and logistic regression

3 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 504 Computer Programming via Q BASIC and FORTRAN 90 Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Flow chart, algorithm, and programming languages: QBasic, Fortran, Pascal, Oracle, SQL, Delphi and biostatistics applications. Using programming language in Biostatistics. Student can use programming language in Biostatistics. Laboratory Work x % 50 Assessment Criteria Instructors Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Basic of programming languages and differences Description of variables and constants and assign a data and using of these data. Input and output flow charts Flow charts of comparison mentality. Flow charts of loops.

4 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Moneybox mentality Using of batches Writing simple algorithms Writing complex algorithms Creating text files, saving, and reading procedures Transfer of flow chart to programming languages. Programming of simple biostatistics applications. Programming of complex biostatistics applications. Preservation of data in database

5 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 505 Simulation Techniques Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish Lectures BİS 510 and BİS 511 ECTS Credit Concept of simulation, random number generation, simulations of Uniform, Normal, Z, T, F and Chi-Square and Multivariate normal distributions. s in Fortran 90 To be able to design a simulation study n Biostatistics. The students will be able to design a simulation study n Biostatistics. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Concept of simulation Random number generation Simulation of Uniform distribution. Simulation of Normal distribution. Simulation of Z distribution.

6 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Simulation of T distribution. Simulation of F distribution Simulation of Chi-Square distribution. Sampling and Hypotheses control via simulation. Sampling and Hypotheses control via simulation. Simulation of Multivariate normal distributions. s in Fortran 90 s in Fortran 90 s in Fortran 90

7 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Using Statistical Methods And Computer Technology in Health Science. Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Objectives Learning Outcomes and Competences Textbook and /or References Searching literature, discussion and using computer technology via seminars. of improvements and interpretation. To join common studies with the other health science departments. Students will be able to search for article, read them and understand the statistical material methods and interpret the results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Searching Literature Searching Literature Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars

8 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars of improvements and interpretation. of improvements and interpretation. of improvements and interpretation. of improvements and interpretation.

9 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 507 Seminar Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Discussing on recent subjects and improvement in Biostatistics Objectives Learning Outcomes and Competences Textbook and /or References Teach the students how to do a research and how to present it. The students will be able to search new subject and grasp the new subject and present it. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing

10 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing

11 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 510 Research Methods in Health Science I Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Objectives Learning Outcomes and Competences Textbook and /or References Basic statistical concepts, types of variables, descriptive statistics, commonly used statistical distributions and their properties. Sampling distributions. Hypotheses tests for one sample, two sample (dependent and independent) groups. To be able to solve basic statistical problems and interpret the results. The students will be able to understand the advanced statistical lectures. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Basic statistical concepts. Types of variables Descriptive statistics Inquire of relationship with table and graphics

12 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Commonly used statistical distributions and their properties. Sampling Distributions Research and sampling methods. Introduction to Hypotheses Testing Hypotheses tests for one sample Hypotheses tests for two independent samples. Hypotheses tests for two dependent samples. Hypotheses tests for k dependent and independent samples. Correlations and different correlation coefficients. Simple and multiple linear regressions.

13 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 511 Research Methods in Health Science II Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 Lecture ECTS Credit Variance analysis models and assumptions, data transformations, simple variance analysis and multiple comparisons, variance analysis of random block design, Latin square design, factorial variance analysis models and covariance analysis. Describing variance analysis experimental designs. Students can choose the best design according to data sets properties. Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Laboratory Work Assessment Criteria Other Committee Exam Instructors Content Week 1 Week 2 Week 3 Week 4 Variance analysis models Assumptions of variance analysis models Data transformations Simple variance analysis and multiple comparisons, variance analysis of random block design, Latin square design, factorial variance analysis models and covariance analysis.

14 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multiple comparisons of simple variance analysis Two-way variance analysis. Analysis of repeated measurements. Analysis of repeated measurements. Variance analysis of random block design Variance analysis of random block design Latin square design Latin square design Covariance analysis. Covariance analysis.

15 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 512 Multivariate Statistical Methods I Program name: Biostatistics Level Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Turkish 510 and 511 Lectures ECTS Credit Basic matrix knowledge, multivariate descriptive statistics, multivariate normal distribution, missing value analysis, multivariate hypotheses testing, multiple linear regression, factor analysis, correspondence analysis, path analysis. Describe the properties of multivariate statistical methods Students will be able to know how to approach multivariate statistical methods 1. Manly, BFJ. Multivariate Statistical Methods: A Primer. Chapman and Hall, Johnson RA and Wichern DW. Applied Multivariate Statistical Analysis.. Prentice-Hall Inc., Özdamar K. Paket Programlar ile İstatistiksel Veri Analizi (Çok Degiiskenli Analizler) 2. Kaan Kitabevi, Eskisehir, Alpar R. Çok Degiskenli İstatistiksel Yöntemlere Giris. Nobel Yayın Dagıtım, Ankara, Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Basic matrix knowledge Multivariate descriptive statistics

16 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multivariate extreme values. Multivariate normal distribution and standardization. Missing value analysis Multivariate hypotheses testing Multivariate hypotheses testing Multivariate two way variance analysis, repeated measures of variance analysis. Multiple linear regression. Factor analysis Factor analysis Correspondence analysis Correspondence analysis Path analysis.

17 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 512 Multivariate Statistical Methods II Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510, BİS 511 and BİS 512 Lectures ECTS Credit Cluster, discriminant, path, multi dimensional scaling and canonic correlation analysis. Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Describe the properties of multivariate statistical methods Students will be able to know how to approach multivariate statistical methods 1. Manly, BFJ. Multivariate Statistical Methods: A Primer. Chapman and Hall, Johnson RA and Wichern DW. Applied Multivariate Statistical Analysis.. Prentice-Hall Inc., Özdamar K. Paket Programlar ile İstatistiksel Veri Analizi (Çok Degiiskenli Analizler) 2. Kaan Kitabevi, Eskisehir, Alpar R. Çok Degiskenli İstatistiksel Yöntemlere Giris. Nobel Yayın Dagıtım, Ankara, Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 ClusterAnalysis ClusterAnalysis ClusterAnalysis

18 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Discriminant Analysis Discriminant Analysis Path Analysis Multi dimensional scaling Multi dimensional scaling Canonic correlation analysis. Canonic correlation analysis. Categorical data analysis. Categorical data analysis. Using package programs Using package programs

19 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 514 Multiple Comparisons Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Parametric and nonparametric multiple comparison methods. Objectives Learning Outcomes and Competences Textbook and /or References Description of parametric and nonparametric multiple comparison methods. The Student will be able to choose proper multiple comparison methods. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Introduction Experimantalwise and Comparisonwise errors. Linear and orthogonal contrasts. Fisher s LSD Tukey s multiple comparison method

20 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Using package programs Student Newman Keuls Procedure Duncan s Multiple Rank Test Using package programs Scheffe s multiple comparison method Bonferroni multiple comparison method Using package programs Nonparametric multiple comparison methods Nonparametric multiple comparison methods

21 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 515 Repeated Measurements Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Description of repeated measurements, assumptions, paired sample t test, simple and advanced repeated measurement designs. Objectives Learning Outcomes and Competences Textbook and /or References Describe the properties of repeated measurements design. Students will be able to know how to approach repeated measurements statistical methods Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Advantages and disadvantages of repeated measurement designs Paired sample t test Repeated measurement designs with one factor Using package programs Repeated measurement designs with two factors and one of them is repeated.

22 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Repeated measurement designs with two factors and two of them are repeated. Using package programs Repeated measurement designs with three factors and one of them is repeated. Using package programs Repeated measurement designs with three factors and two of them is repeated. Repeated measurement designs with three factors and three of them are repeated. Using package programs Covariance analysis for repeated measurement designs Using package programs

23 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 516 Linear Regression Models Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of linear regression models, simple regression models and hypothese controls of model coefficients and variable selection methods and regression diagnostics. Assumption controls of simple and multiple regression analysis, extreme and influence observations. Students will be able to know how to approach linear regression models. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Using vector notations Simple Linear Regression Estimation of simple linear regression coefficients via least square estimation. Statistical significance of simple regression coefficients Using package programs

24 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multiple linear regression Estimation of multiple linear regression coefficients via least square estimation. Using package programs Regression Diagnostics Using package programs Variable selection methods for multiple linear regression. Using package programs Path Analysis Using package programs

25 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 519 Sampling Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of sampling methods, selection of proper sample size, relationship between power and sample size. Objectives Learning Outcomes and Competences Textbook and /or References Decide best the sampling method and calculate the sample size. Students will be able to know how to choose right sampling method and calculate sample size. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Introduction to sampling theory. Estimators and their properties. Sampling distributions and standard error. Point and interval estimation Sample size calculation

26 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Simple random sampling I Simple random sampling II Stratified sampling I Stratified sampling II Systematic Sampling One Step Cluster Sampling Multiple Step Cluster Sampling Weighting of Sampling Non probabilistic Sampling

27 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 517 Special Methods for Health Statistics Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Health statistics, sensitivity, specifity, Roc Curve, relative risk, and odds ratio, Logistic Regression, Survival and Meta Analysis. Objectives Learning Outcomes and Competences Textbook and /or References Description of statistics used for Health Science The Student will be able to choose proper Health statistics for Population. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Health services for Biostatistics Definition of service region Demographic statistics Childbirth statistical methods for speed and rate Statistical methods for diseases

28 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Standardization methods for speed and rate Life tables Statistical methods for preventive studies Statistics for hospital assessment Statistical methods for Dental Health International classification of disease and mortality. Criteria for health levels Health Records

29 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 518 Nonparametric Statistics Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of nonparametric data, descriptive statistics for nonparametric tests, Median test, multiple comparison methods, relationship statistics. To be grasped assumptions and manual calculation of nonparametric statistics. Students can decide the best nonparametric statistics for data sets. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Definition of concepts, types of the scales Chi-Square and Fisher tests One sample Kolmogorov-Smirnov test Using package program Wilcoxen rank test, dependent sample Wilcoxen rank test

30 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Mann-Whitney U test Using package program Rank test and Cochrane q test Kruskal Wallis test Using package program Friedman test Rank korelation test Kendall Tau coefficients Using package program

31 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 520 Nonlinear Regression Analysis Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of nonlinear regression, building up nonlinear models and analysis, growing curves, dose-response models, and using models. Description of nonlinear regression, building up nonlinear models and analysis. Students can built up nonlinear models and interpret the results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Nonlinear regression models Building up Nonlinear regression models Analysis of Nonlinear regression models Using package program Growing curves

32 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Growing curves Using package program 2 2 and 2 3 experimental design Using package program 2 4 Fractional Factorial experimental design Central composite design Non Central composite design Box Behnken design Using package program

33 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 535 Theoretical Distributions I Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Properties of Discrete and Continuous Data ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching students properties of discrete and continuous data Students will know properties of discrete and continuous data Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Mass Combination and permutation Probability Theory Probability Theory Random variable

34 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 One dimension Random variable Expected values and moments Conditional probability, independent events Bayesian Theory Discrete and continuous random variable Distribution functions, conditional probability and distribution functions.

35 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 535 Theoretical Distributions II Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures Properties of Discrete and Continuous Data ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching students properties of discrete and continuous data Students will known properties of discrete and continuous data Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Expected value Moments Bernoulli and Binom distributions

36 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Poisson distribution Geometric and negative binomial distributions Hypergeometric Distribution Characteristic functions Moment functions Conditional expected value, conditional variance

37 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 522 Special Experimental Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Latin square models, models include confounded factors, splitted parcels experimental designs Objectives Learning Outcomes and Competences Textbook and /or References Special experimental designs Students can decide the best experimental design. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Random experimental design Random block design Random block design

38 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Latin Square experimental design Latin Square experimental design Factorial experiments Factorial experiments Combination of factorial experiments and block design Combination of factorial experiments and block design

39 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 523 Bayesian Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Bayes Theory, bayes methods and usage fields, sampling and grouping via bayes. of biasian technique in clinical experiments. Description and usage of Bayesian concept. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Bayes Theory Bayes Theory Usage of Bayesian methods Usage of Bayesian methods

40 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Sampling and grouping via bayes Sampling and grouping via bayes of biasian technique in clinical experiments. of biasian technique in clinical experiments. of biasian technique in clinical experiments. of biasian technique in clinical experiments.

41 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 524 Population Genetics Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Gene frequencies, population balance controls, statistical analysis for dependent and independent genes, gene differences between populations. Preparing student for introduction to statistical analysis. Students will be able to analyze genetic data and interpret results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Gene frequencies Population balance controls Statistical analysis for dependent genes

42 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Statistical analysis for independent genes Gene differences between populations. Gene differences coefficients Gene flow Clustering Analysis

43 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 525 Survey Studies Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Survey methods, preparation of questionnaire ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching survey methods, preparation of questionnaire Student can prepare a survey study and questionnaire. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Survey methods Preparation of Questionnaire. Scaling

44 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Item Analysis Reliability and Validity analysis. Multivariate variance analysis. Factor analysis and survey Factor analysis and survey

45 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 526 Survival Analysis Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Survival analysis, life table, Kaplan Meier and Cox regression, comparison of survival curves Objectives Learning Outcomes and Competences Textbook and /or References Basic knowledge of life data analysis. Student can analyze life data. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Survival time, case and censoring types Estimation methods of Survival time, Kaplan Meier Method Estimation methods of Survival time, Life Table Comparison of survival times

46 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Comparison of survival times Hazard regression Cox regression Variable selection Assumptions

47 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 527 Quality Control Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Turkish BİS 510 and BİS 511 Lectures ECTS Credit Quality Concept in Statistics, Importance of confidence interval in Quality Control, Process Control (SPC), Histogram, Pareto graphs, Fish Bone, Control graphs, Definition of control limits. Teach how to use statistical methods in Quality Control field. Using of statistics in Quality Control field. Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Quality Concept in Statistics Importance of confidence interval in Quality Control Process Control (SPC) Histogram

48 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Pareto graphs Fish Bone Control graphs Definition of control limits

49 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 531 Experimental Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Clinical experimental designs ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Designation of clinical experiments Student can design a clinical experiment Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Design and classification of clinical trial Case series Case control studies Cross Sectional Cohort Studies Comparison of Case control and Cohort Studies

50 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Experimental clinical and designs Trials with independent concurrent control Trial with self controls Trial with external controls Uncontrolled studies Random simple experimental design Trials wit Covariates Trial with two and more factors

51 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 532 Generalized Estimated Equations Level Program name: Biostatistics Master of Sci. Hours/week Ther. Recite. Lab. Others Total MEU Credit ECTS Credit And credit Language Compulsory / Prerequisites Turkish BİS 510 and BİS 511 Lectures Contents Objectives Learning Outcomes and Competences Textbook and /or References Laboratory Work x % 50 Assessment Criteria Other x % 50 Committee Exam Instructors Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Linear models in statistics Week 2 Design matrix Week 3 Quadratic form Week 4 Component of sum of squares Week 5

52 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Relationship between Regression and variance analysis Contrast comparisons Orthogonal comparisons Response surface Balanced blocks Missing blocks

53 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 533 Computer Programming Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Flow chart, algorithm, and programming languages: QBasic, Fortran, Pascal, Oracle, SQL, Delphi and biostatistics applications. Objectives Learning Outcomes and Competences Textbook and /or References Using programming language in Biostatistics. Student can use programming language in Biostatistics. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Basic of programming languages and differences Description of variables and constants and assign a data and using of these data. Input and output flow charts Flow charts of comparison mentality. Flow charts of loops.

54 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Moneybox mentality Using of batches Writing simple algorithms Writing complex algorithms Creating text files, saving, and reading procedures Transfer of flow chart to programming languages. In Structural questioning language, Structural questioning sentences In Structural questioning language, writing and deletion sentences Integration of Structural questioning languages via interfaces

55 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 534 Matrix and Vectors Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Vector and matrix procedures, descriptions, transpose determination, inverse procedures and Using software for these procedures. Calculation matrix procedures Students can calculate matrix procedures by using software. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen Content Week 1 Week 2 Week 3 Week 4 Week 5 Definition of Matrix concept Types of matrixes Summation, extraction and multiplication procedures for matrixes. Determinant Minor, cofactor and ad joint matrixes

56 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Inverse of a matrix Using package programs Orthogonal matrix Division of a matrix Linear dependency and independency concepts Rank concept and nonsingular the biggest sub matrix Linear equation systems Eigen values and eigen vectors Using package programs

### Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

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