Biostatistics III: Survival analysis for epidemiologists (using R)
Doctoral course within the doctoral programme in Epidemiology
Course Number: 2992
Credit points: 1,5
The course aims to introduce statistical concepts and methods for analysing time-to-event data with emphasis on applications in epidemiology and public health.
After successfully completing this course students should be able to:
- propose a suitable statistical model for assessing a specific research hypothesis using data from a cohort study, fit the model using standard statistical software, evaluate the fit of the model, and interpret the results. (S4)
- explain the similarities and differences between Cox regression and Poisson regression. (S3)
- discuss the concept of timescales in statistical models for time-to-event data, be able to control for different timescales using standard statistical software, and argue for an appropriate timescale for a given research hypothesis. (S3)
- discuss the concept of confounding in epidemiological studies and be able to control/adjust for confounding using statistical models. (S3)
- apply and interpret appropriate statistical models for studying effect modification and be able to reparameterise a statistical model to estimate appropriate contrasts. (S3)
- critically evaluate the methodological aspects (design and analysis) of a scientific article reporting a cohort study. (S3)
Learning outcomes are classified according to Bigg's structure of the observed learning outcome (SOLO) taxonomy: (S1) uni-structural, (S2) multi-structural, (S3) relational, and (S4) extended abstract.
This course introduces statistical methods for survival analysis with emphasis on the application of such methods to the analysis of epidemiological cohort studies. Topics covered include methods for estimating survival (life table and Kaplan-Meier methods), comparing survival between subgroups (log-rank test), and modelling survival (primarily Poisson regression and the Cox proportional hazards model). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification.
Literature and other teaching material
Cleves M et al. An Introduction to Survival Analysis Using Stata, 2nd edition. College Station: Stata Press; 2008.
Breslow NE, Day NE. Statistical Methods in Cancer Research: The Design and Analysis of Cohort Studies. Lyon: IARC Scientific Publication; 1993.
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