Learning outcomes include ability to: Define the density, distribution, survival and hazard function for random future lifetimes, and derive relationships between them. Estimate the survival function from data (e.g., using using Kaplan-Meier estimator) and using maximum likelihood estimation. Calculate exposed to risk and crude mortality rates under Binomial and Poisson models, and estimate parameters of well-known laws of mortality. Understand why graduation of crude rates is important and be able to apply several approaches to graduation and assess the results from graduations. Finally, the student will be able to apply several popular approaches to forecasting mortality rates.

Chapter 0: Preface

Chapter 1: Introduction to the Key Concepts, Notation & Mathematics of Survival Models

Chapter 2: Introduction to the Survival Statistics: Censoring & estimating Lifetime Distributions

Chapter 3: Exposed-to-Risk, Binomial & Poisson Models

Chapter 4: Graduating Life Tables

Chapter 5: Methods to Graduating

Chapter 6: Projecting Mortality Rates

Chapter 7: Proportional Hazard models to deal with covariates

Basic statistical knowledge including expectation, variance and maximum likelihood

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Self-assessment activities

After the first two weeks, there will be weekly tutorials where the student can self-assess their grasp of the material and its application in practice. There is an in-class test at mid-term which covers the material done to date in a straightforward manner. This counts 10% to the overall module grade. There is feedback on the mark that each student achieved. The final exam counts 90% to the overall module grade.