Event times and event counts appear in many social and medical data contexts, and require a specialised suite of techniques to handle properly, broadly known as survival analysis. This course covers the basic definitions and techniques for creating life tables, estimating event-time distributions, com-paring and testing distributions of different populations, and evaluating the goodness of fit of various models. A focus is on understanding when and why particular models ought to be chosen, and on using the standard software tools in R to carry out data analysis.
- Life tables
- Kinds of event-time data: Censoring and truncation
- Non-parametric and semi-parametric survival models
- Model diagnostics for survival models
At the end of this course students will…
- Understand the standard notation of life tables, and be able to make inferences from life tables
- Interpret censored and truncated data
- Derive estimators and confidence intervals for standard parametric survival models
- Fit survival curves with standard non-parametric techniques using R, and interpret the results.
- Select appropriate tests for equality of survival distributions, and carry out the tests in R.
- Fit semi-parametric survival models in R and interpret the results.
- Estimate the goodness of fit of survival models using graphical and residual techniques.
- Make individual predictions based on survival data.
- Introduction to survival data: hazard rates, survival curves, life tables.
- Censoring and truncation, introduction through the census approximation.
- Parametric survival models
- Non-parametric estimation of survival curves
- Non-parametric model tests (log-rank test and relatives)
- Semi-parametric models
- Model-fit diagnostics
- Dynamic prediction and model information quality
- Repeated events