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The University of Southampton
Courses

MATH3085 Survival Models

Module Overview

This module introduces some of the fundamental ideas and issues of lifetime and time-to-event data analysis, as used in actuarial practice, biomedical research and demography.

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Understand the key features of lifetime data, including censoring, and of probability models for lifetime data: survival function, hazard and force of mortality
  • Understand the theory underlying survival models and their estimation and of how to use R to fit models, including the Kaplan-Meier estimate of the survival function, parametric models and the Cox regression model
  • Understand models for human mortality, including how to compute and interpret the life table in a variety of contexts and models for forecasting morality.
  • Describe and apply models of mortality and similar events which are specified as continuous-time discrete-state Markov processes
  • Understand the need to graduate crude data on mortality rates and an understanding of how to compare crude mortality rates against a standard of graduated set of rates
  • analyse and interpret data, and especially to adopt a critical approach to numerical data
  • solve problems, and especially to apply ideas learnt in one context to other contexts

Syllabus

• 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

Learning and Teaching

Teaching and learning methods

Lectures, problem classes, office hours, computer laboratories.

TypeHours
Teaching60
Independent Study90
Total study time150

Resources & Reading list

D. Collett (2003). Modelling survival data in medical research.. 

Reading List. A suggested reading list for each part, and starting references for each assessment, will be made available on Blackboard

A. Hinde (1998). Demographic methods. 

Assessment

Summative

MethodPercentage contribution
Class Test 10%
Data analysis project 20%
Exam  (120 minutes) 70%

Referral

MethodPercentage contribution
Exam 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: MATH2011 OR ECON2006

Costs

Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Course texts are provided by the library and there are no additional compulsory costs associated with the module.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.

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