• Introduction to concepts of modelling, survival data and survival models; censoring; survival and hazard functions.
• Estimating the survivor function non-parametrically (Kaplan-Meier and Nelson-Aalen estimators); parametric survival models; estimation using maximum likelihood.
• Regression models for survival data; proportional hazards; the Cox regression model; accelerated failure time models.
• Introduction to continuous-time, discrete-state Markov models; two-state and multiple-state models; Kolmogorov equations; estimating the parameters of multiple-state models.
• Models for human mortality; the life table: theory and applications.
• Comparison of models of mortality: Binomial, Poisson and multiple-state models. Estimation and inference using maximum likelihood and other methods.
• Exposure to risk; the principle of correspondence; estimating the exposed-to-risk with aggregate data.
• Comparison of mortality experiences; mortality rates and standardised mortality ratios; statistical tests appropriate for the comparison.
• Graduation of mortality data; reasons for graduation; methods of graduation; tests of adherence to data and smoothness of a graduation.
• Models for forecasting human mortality
• Using R to analyse lifetime and survival data