Further Topics in Medical Statistics

Module code: MD7447
Module coordinator: Stephanie Hubbard

If you select this module you will be able to select two option teaching weeks from a range of different topics. The specific range of topics available varies from year to year. 

Topics available to current students include:

Advanced Survival Analysis

This option builds on the methods covered in the (basic) survival analysis module. The methods you cover will include the use of counting process notation, time-dependent covariates, modelling time-dependent effects, multiple events, cure models and flexible parametric survival models. There will be lectures, group practical work and presentations.

Advanced Evidence Synthesis

This option will start with a review of basic meta-analysis for different outcomes. We will then explore Bayesian implementation of meta-analysis and meta-regression methods, using the WinBUGS software, and discuss the advantages of using this approach. More advanced methods which we will look at include meta-analysis methods for the evaluation of diagnostic tests, and mixed treatment comparisons (or network meta-analysis) which is a very important generalisation of the standard meta-analysis model increasingly used in HTA when more than two alternative treatment options exist.

Genetic Epidemiology

In this option you will be introduced to the basic biology and genetics, and statistical and epidemiological concepts that underpin genetic epidemiology. Practical sessions will give you experience of running and interpreting a genetic association analysis including multiple genetic variants. We will cover the fundamental concepts underpinning analyses of family-based data, recurrence risk ratios, heritability and variance components analysis, and will consider the use of genetic data in causal inference through 'Mendelian Randomisation'. 

Decision Modelling

This week will start by outlining the motivation for, and principles of, decision modelling. We will cover clinical decision models (weighing up benefits and side-effects of treatments) as well as economic decision models (assessing cost-effectiveness), using first simple decision trees and then Markov models. After exploring the basic principles of decision modelling, we will discuss methods for estimating individual parameters for such models, including the integration of meta-analyses with decision models. Given that there is usually (considerable) uncertainty in any decision problem, we will explore the comparative value of information methods to assess whether conducting further studies to reduce this uncertainty would provide good value for money.

Statistical Programming in SAS

SAS is an extensive software package used in the pharmaceutical industry, by clinical trials units and by some researchers. It has historically been used as the recommended software for data management, data reporting and data analysis of clinical trials. In this option you will learn how to use SAS for data handling and statistical analysis through an introductory lecture, a self-learning booklet and support sessions.

Multivariate Analysis

A series of short lectures will present you with overviews of different multivariate techniques along with practical examples. Practical sessions will then let you apply these techniques while reading further about the methods. You will select one technique to study in detail: its theory, it application in medicine and a critique of a published application. Possible topics include: Multivariate Modelling, Principal Component Analysis, Factor Analysis, Discriminant Analysis, Cluster Analysis and Canonical Correlation.

Directed reading

If you choose this week you will use your own knowledge and experience to identify a topic related to medical statistics and find a published article to act as a starting point. Under the supervision of a member of staff, you will then read around the topic and prepare a report. We will be happy to help you select the topic and the initial reference.

Learning

Learning will depend on your choice of topic, but is likely to be about 21 hours of lectures and practicals for each of the two weeks.

Assessment

  • Coursework 1 (50%)
  • Coursework 2 (50%)