Programme And Module Handbook
Course Details in 2021/22 Session

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Module Title LM Medical Statistics
SchoolInst of Applied Health Res
Department Inst of Applied Health Res
Module Code 06 22762
Module Lead Dr Yemisi Takwoingi
Level Masters Level
Credits 20
Semester Semester 2
Pre-requisites Statistics II - (06 22506) Statistics II - (06 22510)
Restrictions None
Contact Hours Lecture-46 hours
Tutorial-10 hours
Total: 56 hours
Description This module will give a comprehensive introduction to medical statistics and show why it is an important application of mathematical and statistical theory. The module will use statistical methods learnt in other modules, and also introduce some new statistical concepts and methods, with application to real medical problems and healthcare research. The module is split into 5 sections. In Epidemiology, students will cover the analysis of diagnostic test studies; the design and analysis of cohort studies and case-control studies; the use and interpretation of linear and logistic regression models; and the problem of confounding factors. In Survival Analysis students will consider how to analyse censored time-to-event data; the specification and interpretation of hazard and survival functions; the estimation of Kaplan-Meier curves; and the use and interpretation of Cox regression models. In Clinical Trials students will consider the rationale and design of randomised controlled trials; the derivation and application of sample size equations; and the analysis of parallel group trials and cross-over trials. In Systematic Reviews students will consider how to synthesise multiple studies and produce evidence-based results; show the differences between fixed-effect and random-effects meta-analysis models; show how to measure, account for, and explain between-study heterogeneity; and appreciate how to assess publication bias. Finally, in Bayesian Statistics students will cover the differences between Bayesian and frequentist statistical inference; work through the Bayesian analysis of a clinical trial; derive and interpret posterior probability distributions; and utilise the computer software FirstBayes.
Learning Outcomes By the end of the module the student should be able to:
  • Appreciate how medical statistics plays a fundamental role in understanding health and diseases in the population;
  • Understand why medical statistics informs clinical practice and the screening, diagnosis, and treatment of patients;
  • Understand the rationale for and design of cohort studies, case-control studies, clinical trials, and systematic reviews;
  • Understand, apply and interpret a range of statistical methods in the context of epidemiological studies, diagnostic tests, clinical trials, survival analysis, and systematic reviews;
  • Apply mathematical theory to derive unbiased estimates of disease risk in different groups of patients, and to interpret their uncertainty by calculating confidence intervals and p-values;
  • Understand and interpret regression models for continuous, binary and survival data;
  • Understand the differences between Bayesian and classical statistical inference, and perform Bayesian statistical analyses of medical data;
  • Understand new developments in medical statistics.
Assessment 22762-01 : Raw Module Mark : Coursework (100%)
Assessment Methods & Exceptions 80% based on a 3 hour written examination in the Summer Term; 20% based on work during term-time.
Other None
Reading List