Further Topics in Medical Statistics

Module code: MD7447

If you select this option you will be able to select two option teaching weeks from a range of different topics. The choice will vary from year to year and your choice will be restricted to selecting options offered in different teaching weeks.

Currently some of the topics offered are:

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: The option will start with a review of basic meta-analysis for different outcomes. You will then use a Bayesian implementation of meta-analysis and meta-regression methods, using the WinBUGS software and discuss the advantages of using this approach. More advanced methods such as mixed treatment comparisons (or network meta-analysis), a very important generalisation of the standard meta-analysis model increasingly used in HTA when more than two alternative treatment options exist, and meta-analysis methods for the evaluation of diagnostic tests will be introduced.

Genetic Epidemiology 1: 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. The fundamental concepts underpinning analyses of family-based data, recurrence risk ratios, heritability, and variance components analysis will be covered. The use of genetic data in causal inference through “Mendelian Randomisation” will be considered.

Decision Modelling: This week will start by outlining the motivation for and principles of decision modelling. Both clinical decision models (weighing up benefits and side-effects of treatments) and economic decision models (assessing cost-effectiveness) will be covered. Simple decision trees and then Markov models will be described. After the basic principles of decision modelling are covered, methods for estimating individual parameters for such models will be considered. This includes the integration of meta-analyses with decision models. Given there is usually (considerable) uncertainty in any decision problem, value of information methods, to assess whether conducting further studies to reduce this uncertainty would provide good value for money, will be presented briefly.

Statistical Programming in SAS: SAS is an extensive software package used in the pharmaceutical industry, 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. This option will give you the opportunity to learn to use SAS for data handling and statistical analysis through an introductory lecture, a self-learning booklet and support sessions.

Data Science (new for 2018): There will be a series of short lectures presenting overviews of a number of different topics. These will include: an overview of big data, sources, visualisation and the issues in handing and analysing big data; statistical methods, for example principal components, factor and cluster analysis; machine learning, for example random forests, neural nets, classifiers, support vector machine, naïve Bayes classifier.

Student Selected Reading: If you choose this week you identify a topic related to medical statistics and find a published article to act as a starting point. Under the supervision of one of the staff, you read around the topic and prepare a report. We will be happy to help you select the topic and the initial reference.

Learning

  • 16 hours of seminars
  • 32 hours of practicals
  • 102 hours of independent study

Assessment

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