Advanced Statistical Modelling
Module code: MD7444
Module co-ordinator: Professor Paul Lambert
Survival analysis is concerned with data where we measure the time to an event. In medicine we are often interested in death, i.e. we want to keep people alive and make them live longer. We thus measure the time (from some suitable starting point) to death. The outcome of interest is the time to an event and we are unlikely to observe the event on all subjects.
- Introduction to Survival Analysis – censoring, the survival function, the Kaplan-Meier estimate, median survival, life-tables
- Comparing Survival Curves - the log rank test, hazard ratio
- Modelling Survival - proportional hazards, exponential model, Weibull model, the Cox model, partial likelihood, tied observations, interpreting regression coefficients
- Model fitting and selection
- Model checking and regression diagnostics for the Cox model
Introduces students to multilevel modelling for the analysis of hierarchical and repeated measures data for both continuous and binary outcomes. Students will have practical experience of working with a range of software packages and will also critique articles published in the medical literature using these techniques.
- Introduction to multilevel (hierarchical) data structures
- Summary methods
- Introduction to multilevel modelling and MLwiN
- Multilevel models for longitudinal/repeated measures data.
- Multilevel models using Stata
- Repeated binary data analysis using MLwiN
- Alternative analysis: Generalised Estimating Equations (GEEs)
- 20 one-hour lectures
- 28 one-hour workshops
- Exam, 90 minutes (40%)
- Coursework 1 (30%)
- Coursework 2 (30%)
Participation in the Statistical Modelling module, or equivalent experience or qualification