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| TBB6010 | Advanced Biostatistics | 2+2+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To get the ability of the use of advanced biostatistic approaches with R in the analysis of data. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Ability to cluster biological data with R | | 1,3 | | PO - 2 : | Ability to analyze data using advanced biostatistic approaches | | 1,3 | | PO - 3 : | Ability to interpret the results of analyze based on advaced biostatistic approaches | | 1,3 | | PO - 4 : | Ability to use advanced biostatistic approaches with R | | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Probability, expected values, conditional probability, Bayes rule, confidence intervals, bootstrapping, hypothesis testing, probability, relative risk, odds ratio and R applications |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Probability and probability functions (probability mass, probability density and cumulative distribution of functions) | | | Week 2 | Expected values, variances, standard deviations, individual events, variables, covariance and correlation | | | Week 3 | Conditional probabilities and Bayes' rule | | | Week 4 | Likelihood and ratio benchmarks | | | Week 5 | Limits, the central limit theorem, and confidence ıntervals | | | Week 6 | Chi-squared and t-distributions, confidence intervals, and likelihoods | | | Week 7 | Mid-term examination | | | Week 8 | Bootstrap principle, algorithm, and calculations | | | Week 9 | Hypothesis testing, power, two sample tests | | | Week 10 | Relative risks, odds ratios and delta method | | | Week 11 | Fisher's exact test | | | Week 12 | Simpson's pardox, weighting, Mantel/Haenszel estimator | | | Week 13 | Case control sampling and exact inference for odds ratio | | | Week 14 | 2x2 Tables | | | Week 15 | Sign test, Sign rank test, Monte Carlo, Mann/Witney test and permutation tests | | | Week 16 | Final examination | | | |
| 1 | Larry Hatcher, 2013; Advanced Statistics in Research: Reading, Understanding, and Writing Up Data Analysis Results, | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | | | | | | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Arasınav için hazırlık | 2 | 6 | 12 | | Arasınav | 2 | 1 | 2 | | Ödev | 3 | 14 | 42 | | Dönem sonu sınavı için hazırlık | 2 | 16 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 216 |
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