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| Course Description |
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| Course Name |
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Lineer Models |
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| Course Code |
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BİS531 |
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| Course Type |
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Optional |
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| Level of Course |
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Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
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Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. HÜSEYİN REFİK BURGUT
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| Learning Outcomes of the Course |
: |
Linear models for binary and count data ; Logistic, Poisson, Log Linear
ANOVA-ANCOVA- fixed and random effect linear models for continuous data
Generalized linear models for categorical data
Mixed linear models for continually continuous data
Linear models for repeated continuous data
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
BİS540 Applied Biostatistics
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To know the various linear models to analyze data obtained from the epidemiological and clinical studies, to determine the appropriate one related to the properties of data, to interpret the results of the application |
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| Course Contents |
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Linear models and data sets, classical linear models;regression, anova and ancova, generalized linear models, link functions,: multiple regression, linear regression models for longitidunal data, poisson regression, marginal and transitional models, competing risk models |
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| Language of Instruction |
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Turkish+English |
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| Work Place |
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Informatic lab in Biostatistics Dept. |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
WHY USE MODELING IN DATA ANALYSIS? Basic terminalogies |
reading the first chapters in reference book #1 |
reading |
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2 |
Data sets to be used as an example in linear modeling: REFUGEE CHILDREN GROWTH, MOTHER STRESS- CHILDREN MORBIDITY, NUMBER OF SEXUAL PARTNERS CHANGİNG |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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3 |
CLASSICAL LINEAR MODELS; REGRESSION, ANOVA AND ANCOVA |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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4 |
GENERALIZED LINEAR MODELS;RANDOM COMPONENT, SYSTEMATIC COMPONENET, EXPONENETIAL DISPERTION FAMILY, LINK FUNCTION |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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5 |
SPECIAL CASES; MULTIPLE REGRESSION, LOGISTIC REGRESSION
POISSON REGRESSION |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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6 |
LIKELIHOODS, MARGINAL, PARTIAL PSAUDO VE QUASI LIKELIHOOD |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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7 |
LONGITUDINAL DATA AND EXAMPLE-LINEAR MODELS |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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8 |
MID TERM EXAM |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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9 |
MARGINAL MODELS-TRANSITIONAL MODEL |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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10 |
RANDOM EFFECT MODELS |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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11 |
INFERENCE USING GENERALIZED ESTIMATION EQUATION (GEE) |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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12 |
MODELS FOR TIME TO EVENT DATA |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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13 |
REPEATED TIME TO EVENT DATA- ANDERSON AND GILLS MODELS, MARGINAL MODELS, FRAİLTY MODELS |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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14 |
COMPETING RISK MODELS |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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15 |
ADDITIVE MODELS |
reading the related chapters in reference boks #2 and #3 |
reading and assignment |
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16/17 |
FINAL EXAM |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
14 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Students design scientific research studies in order to give response to the problem arising from health and clinical sciences |
0 |
|
2 |
Students provide consulting services by using effective communication skills; take part in research teamworks; defend the ethical rules. |
0 |
|
3 |
Students collect data from research studies, analyze, and make inferences |
4 |
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4 |
Students design health survey, determine the sampling method and conduct the survey |
0 |
|
5 |
Students knows the system of international classification of diseases, obtain and analyze hospital statistics. |
0 |
|
6 |
Students select the appropriate statistical procedure for analysis , apply and make inferences. |
4 |
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7 |
Students use the necessary statistical packages for analysis, if necessary write and develop software. |
4 |
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8 |
Students select and use proper statistical procedure for diagnosis and in making inferences for the data in health and clinical medicine and provide consultance to clinicians in the field. |
0 |
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9 |
Students comprehends the fundamentals of statistical theory related to the field of health ( probability and bayesian biostatistics). |
0 |
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10 |
Students explain demographic terminologies and statistical methods in the field of health sciences. |
0 |
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11 |
Students understand and use medical terminology. |
0 |
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12 |
Students develop the ability of critical thinking, make a conclusion with a critical approach to the evidence |
0 |
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13 |
Students apply analytical procedure to frequently used survival data, multivariate procedure and regression techniques. |
0 |
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14 |
Students follow the latest development in medical informatics and employ frequently used tools and methods. |
0 |
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15 |
Students explain the fundamental terminologies in epidemiology, guide researchers conducting field survey and clinical studies, develop methodologies in determining disease risk factor and disease burden and advise for choosing proper diagnostic test. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
14 |
4 |
56 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
3 |
3 |
|
Final Exam |
1 |
12 |
12 |
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Total Workload: | 155 |
| Total Workload / 25 (h): | 6.2 |
| ECTS Credit: | 6 |
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