Introduction to survival analysis, spring 2017
Introduction to survival analysis, spring 2017
5 cr
Register for the course in Weboodi
Lecturer
Nikoloz Maglaperidze
Course description
Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modeling in economics, and event history analysis in sociology.
Survival analysis involves the modeling of time to event data; in this context, death or failure is considered an "event" in the survival analysis literature – traditionally only a single event occurs for each subject, after which the organism or mechanism is dead or broken.
The course provides an introduction to a variety of statistical methods that not only are key elements of survival analysis but also are central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, analytic modeling and likelihood methods will be discussed in the context of survival data. Discussion of such statistical concepts as bias, confounding, independence presented in the context of survival analysis are basic to a broad range of applications.
Prerequisites
Students are expected to understand elementary calculus and basic statistical methods.
Teaching Schedule
The course will involve eight 90-minute lectures:
Week 3 | ||||
|---|---|---|---|---|
Lecture 1 | Tue | 17.1.2017 | 9-11 | |
Lecture 2 | Wed | 18.1.2017 | 9-11 | |
Lecture 3 | Thu | 19.1.2017 | 9-11 | |
Lecture 4 | Fri | 20.1.2017 | 9-11 | |
Week 4 | ||||
Lecture 5 | Mon | 23.1.2017 | 9-11 | |
Lecture 6 | Tue | 24.1.2017 | 9-11 | |
Lecture 7 | Wed | 25.1.2017 | 9-11 | |
Lecture 8 | Thu | 26.1.2017 | 9-11 | |
The topics of the lectures:
- Lecture 1: Rates and their properties.
- Lecture 2: Life tables.
- Lecture 3: Duration of life as a random variable. Models of distribution functions and force of mortality.
- Lecture 4: The empirical distribution function of duration of life. The Kolmogorov goodness-of- fit tests. Confidence band for the population distribution function.
- Lecture 5: The empirical distribution function of duration of life. The Kolmogorov goodness- of-fit tests. Confidence band for the population distribution function. Testing exponentiality.
- Lecture 6: Censored observations. Kaplan-Meier estimation (product-limit estimation). Nelson- Aalen estimation.
- Lecture 7: Simple regression methods.
- Lecture 8: Simple Cox (Proportional Hazards) regression: The concept of Proportional hazards.
The lectures can be found in the Moodle.
Assessment and grading policy
Home work 1=20%
Home work 2=20%
Home work 3=20%
Course Project=40%
Home works
The home works cover the material from that week and previous week. The home works don't always exactly correspond to the material for that week alone. However, the material is always covered before the home work is due.
The home works can be found in the Moodle.
Course project
The Course Project is an opportunity to demonstrate the skills students have learned during the course.
General reading
- Steve Selvin. (2008). Survival Analysis for Epidemiologic and Medical Research Practical Guides to Biostatistics and Epidemiology. Cambridge University Press.
- Estáte V. Khmaladze (2013) Statistical Methods with Applications to Demography and Life Insurance. Chapman and Hall/CRC.