Description
The course provides an introduction to models and methods used in event-history analysis (also known as survival analysis, hazard regression, intensity regression, or duration data analysis). The content of this course can be applied to address many research questions in demography, social sciences, and epidemiology. For example, do education or grip strength predict how long people live, how long they stay unemployed, or when they marry or have their first child? The course also provides an introduction to R programming language appropriate for analyzing survival data.
Content
This course deals with methods of analyzing survival times or time-to-event data, which may be censored and/or truncated. The main topics are estimating a life table, estimating healthy life expectancy, estimating a survival curve, comparing two (or more) survival curves, and regression analysis.
Competencies
By the end of the course, students should be able to:
- Describe the basic concepts of event-history analysis, reflect on the assumptions, problems, and limitations of event-history methods
- Understand the link between event-history analysis, basic demographic methods, and regression analysis
- Recognize the type of research questions for which event history analysis would be a suitable method
- Interpret studies that have used basic event-history methods
Students completing the course paper should additionally be able to:
- Transform data into the basic data layout of event history analysis
- Analyze time-dependent univariate and multivariate relationships
- Specify appropriate regression models using time-constant and time-varying explanatory variables
- Interpret results obtained and communicate them to experts and non-experts alike
This Module is built upon Modules 40-MPH-2 (MPH11, MPH21), und 40-MPH-7h (MPH22). It also is highly recommended that students participate in the Module MPH8a (MPH31). Module 40-MPH-8h (MPH32) is a more applied course, though a brief overview of the theoretical part will be covered. Knowledge of a statistical software (e.g. R, STATA) is not mandatory, but it is advantageous.
Main literature:
1. Blossfeld H-P, Rohwer G., Schneider T (2019). Event history analysis with Stata. 2nd edition: Routledge
2. Broström, G. (2021). Event history analysis with R. Chapman and Hall/CRC.
Additional books:
1. Allison, P. (1984) Event History Analysis.
2. Kleinbaum, David G./Klein, Mitchel (2005): Survival Analysis: A Self-Learning Text. 2nd Edition. New York: Springer.
3. Cleves, M., Gutierrez, R.G., Gould, W. & Marchenko, Y.V. 2010. An Introduction to Survival Analysis Using Stata. Stata Press.
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum | |
---|---|---|---|---|---|
wöchentlich | Do | 8-12 | 07.10.2024-31.01.2025 |
Modul | Veranstaltung | Leistungen | |
---|---|---|---|
40-MPH-8h Methoden demografischer Analyse - Globale Aspekte | MPH32 Methoden der demografischen Analyse - Globale Aspekte | Studienleistung
benotete Prüfungsleistung |
Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
Students are required to complete several assignments (SLs) and to write a research paper to complete the examination (PL) and collect the course credits. The form of examination is a course paper which should be prepared following "Instruction for authors" of a peer-reviewed journal, more specifically the Journal of Epidemiology and Community Health (https://jech.bmj.com/pages/authors/#original_research). The deadline for submission of the course paper is 31.03.2025.
Die Modulprüfung (course paper) wird in Form einer Gruppenarbeit erbracht (vgl. § 10 Abs. 6 prüfungsrechtliche Rahmenrichtlinien der Uni Bielefeld). Durch entsprechende Einteilung der Studierenden in Gruppen und individuelle Vergabe der Aufgabenstellungen stellt der*die Prüfer*in sicher, dass der als Modulprüfung zu bewertende Beitrag der*des Einzelnen bewertbar ist und die in den Regelungen zum Curriculum geregelten Anforderungen erfüllt. Die Prüfungsleistung/Studienleistung eines Studierenden in der Gruppe muss auf Grund der Angabe von Abschnitten, Seitenzahlen oder anderen objektiven Kriterien, die eine eindeutige Abgrenzung ermöglichen, deutlich unterscheidbar und erkennbar sein.