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Continuing Professional Development: MSc Programme Modules (London-based)

SURVIVAL ANALYSIS AND BAYESIAN STATISTICS (2463)

ORGANISERS: Dr Lois Kim, Dr David Prieto

22 February – 23 March 2012 (Wednesdays 2pm to Fridays 5pm)


AIM
To equip students with the necessary skills to understand the principles and apply the techniques of Survival Analysis and Bayesian Statistics.

Note: The pairing of Survival Analysis and Bayesian Statistics in this module is for administrative reasons. Although Bayesian techniques are applicable to Survival Analysis, the two components of the module should be treated as distinct.

 

OBJECTIVES
By the end of this module students should be able to:

  1. demonstrate an understanding of the theoretical basis of Survival Analysis;
  2. use Survival Analysis for analysis of data as appropriate;
  3. demonstrate an understanding of the theoretical basis of Bayesian Statistics;
  4. describe the fundamental differences between the Bayesian and frequentist approach.


CONSTITUENCY
This module is intended for people with both mathematical (up to first year undergraduate level) and statistical backgrounds (undergraduate degree level in joint mathematics/statistics for example) intending to pursue a career in medical statistics.  A knowledge of linear regression, analysis of variance, likelihood theory and simple methods of analysing quantitative and categorical data is essential.  Familiarity with STATA is advised but no previous knowledge of Bayesian statistics is necessary.


CONCEPTUAL OUTLINE
Topics to be covered are:

  1. Survival Analysis: Non parametric and parametric estimation of survival curves, the hazard function and maximum likelihood estimation. The theory and use of proportional hazard models, time dependent covariates. Accelerated failure time models and other more general survival models.
  2. Bayesian statistics: Bayes’ theorem and its application to data analysis.  Prior and posterior distributions.  Sources of prior knowledge, non-informative and conjugate priors.  Reporting the results of Bayesian analyses.    Comparisons of the Bayesian and frequentist approach.


TEACHING STRATEGY
Learning will generally be based on relevant practicals following lectures.  Some sections of each module will involve the use of computers and/or groupwork. Assignments will also be given as part of the practical work. Approximately half of the contact time will be spent in the form of practicals.  Time will be allocated for private study sessions.


LEARNING TIME
The module is made up of 150 Notional Learning Hours – 50 hours contact time, 30 hours directed self-study, 20 hours self-directed learning, and 50 hours assessment, review and revision.

ASSESSMENT
Students will carry out one formal and one practical assessment, each consisting of an analysis of data or a programming exercise together with submission of a short report.

FEE

£1,600 including access to LSHTM library and learning resources, study materials and assessment.

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