You will study 180 or 185 credits in total during your Medical Statistics MSc. A standard module is typically worth 15 credits and the research project is worth 60 credits. These are the modules studied in 2019.
Introduction to Clinical Trials - 15 credits
The module will provide an outline to the statistical principles of clinical trial design, conduct, analysis and reporting. The emphasis will be on understanding the practical issues that arise through real examples backed up with the relevant theory. Pre requisite knowledge of linear modelling is required.
Introduction to Health Data Science - 15 credits
Provides students with a thorough grounding in the principles of planning, conducting, and critically reviewing data and scientific research in the contexts of health of medicine. By the end of the module, students will be confident with the language and conventions of health data science, calculating and interpreting measures of occurrence and association, designing and evaluating scientific studies in populations, identifying and appraising sources of bias, and using causal diagrams to support causal reasoning.
Modelling Prediction and Causality with Observational Data - 15 credits
Offers a comprehensive introduction to linear modelling and the skills and knowledge necessary to analyse various outcome data types. By the end of the module students will be able to identify suitable linear models for analysing a variety for different outcome types; fit a linear model using statistical software including selection of model parameters; compare between models and assess the appropriateness or otherwise of the fitted model.
Statistical Computing - 15 credits
The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems and for analysing very large data sets."Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model.
Students will also undertake one of the following sets of two modules - either:
Dissertation in Statistics - 60 credits
Each student will discuss with an individual supervisor a suitable research project. The title and objectives of the project will be approved by the Programme Manager.
Independent Learning and Skills Project - 15 credits
Students will be able to develop a systematic search strategy to find material on a given topic, using Mathematical word processing and evaluation of material, referencing conventions.
Students taking the research project module will learn the skills and knowledge to conduct research in an applied area of epidemiology and biostatistics. Working within one of the established research groups, this module will allow students to experience the interdisciplinary nature of research.
Professional Skills for Health Data Analysts - 15 credits
Students will learn how to work as a professional health data analyst or biostatistician, e.g. critically appraising the work of others and producing work of publishable quality, working ethically and liaising appropriately with other medical professionals.
Optional modules include
By the end of the module students will be able to critically appraise statistical models as presented in the literature, identifying modelling strategies that are potentially erroneous, understanding alternative strategies (if they exist) that avoid mathematical coupling, reversal paradox and inappropriate modelling of compositional data and composite variables.
Focuses on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.
Further Techniques in Health Data Analytics - 15 credits
Offers an understanding of advanced techniques which underpin the study of diseases in a population, including genetic and geographical epidemiology. Students will learn to undertake survival analyses, analysis of diagnostic tests, meta-analyses and conditional logistic regressions.