AKT703 - SURVIVAL ANALYSIS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SURVIVAL ANALYSIS | AKT703 | 1st Semester | 3 | 0 | 3 | 10 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Drill and Practice | |||||
| Instructor (s) | Department Instructors | |||||
| Course objective | The aim of this course is to provide a statistical investigation of random lifetimes or survival models. | |||||
| Learning outcomes |
| |||||
| Course Content | Estimating the lifetime distribution: cohort studies, censoring, cross-sectional studies; The Kaplan-Meier estimation of the survivor function; Cox regression model; 2-state and multi-state Markov models; binomial and Poisson models of mortality; calculation of exposure: homogeneity, the principle of correspondence, calculation of exposure from census data, adjustment of census data | |||||
| References | 1. Macdonald, A.S (1996), An actuarial survey of statistical models for decrement and transition data I: Multiple state models, Poisson and Binomial models, BAJ 2,I, 129-155. 2. Macdonald, A.S (1996), An actuarial survey of statistical models for decrement and transition data II: Competing risks, nonparametric and regression models, BAJ, 2,II, 429-448. 3. Macdonald, A.S (1996), An actuarial survey of statistical models for decrement and transition data III: Counting process models, nonparametric and regression models, BAJ, 2,III, 703-726. 4. Cox, D.R. (1972), Regression models and life tables, Journal of Royal Statistical Society, Series B, 34, 2, 187-220. 5. Collett, D. (2003), Modelling Survival Data in Medical Research, Chapman and Hall/CRC 6. Lecture notes | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to survival models, notation and revision |
| Week 2 | Simple survival model, estimating the lifetime distribution: cohort studies, censoring, cross-sectional studies |
| Week 3 | The Kaplan-Meier estimation of the survivor function, standard error of Kaplan-Meier estimate; applications in R |
| Week 4 | Cox regression model, the partial likelihood function, parameter estimation, hypothesis testing and likelihood ratio test, score test and Wald test |
| Week 5 | Markov models: theory, fundamental assumptions, multi-state Markov models, Kolmogorov forward equations |
| Week 6 | Markov models: data and estimation, data and 2-state model, the MLE of the force of mortality |
| Week 7 | Midterm Exam |
| Week 8 | Markov models: estimation in the 3-state model, the likelihood in multi-state models, properties of the MLE?s |
| Week 9 | Binomial and Poisson models of mortality |
| Week 10 | Exposed to risk, homogeneity, the principle of correspondence, exact calculation of exposure |
| Week 11 | Calculation of exposure from census data, the life year rate interval, the calendar year rate interval, the policy year rate interval |
| Week 12 | Adjustment of census data, comparison of life/calendar/policy rate intervals, application in R |
| Week 13 | Presentation of posters |
| Week 14 | Presentation of posters |
| Week 15 | Preparation for Final Exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 10 | 10 |
| Presentation | 1 | 20 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 12 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 5 | 65 |
| Presentation / Seminar Preparation | 1 | 33 | 33 |
| Project | 0 | 0 | 0 |
| Homework assignment | 10 | 9 | 90 |
| Midterms (Study duration) | 1 | 30 | 30 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 40 | 120 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Develop new strategies and techniques in modelling actuarial problems and produce solutions specific to a problem. | X | ||||
| 2. Conduct detailed research in a specific subject in actuarial science area. | X | ||||
| 3. Have the required level of competency in actuarial sciences to be able to make contribution to the actuarial literature. | X | ||||
| 4. Able to use the knowledge in actuarial sciences in multidisciplinary studies. | X | ||||
| 5. Arrange events and projects in actuarial sciences . Able to conduct the stages of designing, executing and reporting results of a project. | X | ||||
| 6. Have scientific scepticism. | X | ||||
| 7. Able to produce scientific publications in the area of actuarial science. | X | ||||
| 8. Able to think analytically. | X | ||||
| 9. Follow national and international innovations and improvements in the area. | X | ||||
| 10. Follow actuarial literature | X | ||||
| 11. Improve foreign language skills in order to do work and presentation in that language. | X | ||||
| 12. Use information technology in an advanced level. | X | ||||
| 13. Able to work individually and have the ability to decide independently. | X | ||||
| 14. Have the qualifications necessary for a team work and able to be the team leader. | X | ||||
| 15. Have the consciousness of professional and social responsibility. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest