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This subject presents methods for the analysis of data where outcome events occur over time, particularly in the context of a cohort study or more general longitudinal designs. It starts with the estimation of constant rates and two-group comparison of rates using the rate ratio, and progresses to the use of life tables and the Kaplan-Meier procedures to estimate a survival curve when rates are not assumed to be constant over time and (possibly right-censored) time-to-event data are available. Much of the subject is devoted to studying Poisson and proportional hazards (Cox) regression methods that allow adjustment for confounding variables when comparing rates between two or more primary exposure groups. Emphasis is on practical application and interpretation of results in the context of standard epidemiological study designs and particularly longitudinal studies. Practical work estimating rates and fitting models to data will use the statistical package Stata.
Intended learning outcomes
On completion of this subject, students are expected to be able to:
- Calculate a rate using time-to-event data and compare rates between groups (and draw appropriate inferences)
- Implement the life table and Kaplan-Meier procedures for estimating survival curves both manually and with the use of a computer.
- Describe the role of regression modelling of rates in epidemiology, particularly in the context of cohort and other longitudinal studies
- Demonstrate practical skills in fitting and interpreting regression models for events over time (Poisson and Cox regression models) in the statistical computing package Stata
- Recognise that the proportional hazards (Cox) regression model is a special case of both Poisson regression (for rates) and conditional logistic regression (for matched case-control sets).
At the completion of this subject, students will have developed skills in:
- Critical thinking and analysis
- Finding, evaluating and using relevant information
- Written communication
- Using computers
Last updated: 18 December 2020