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Statistical methods make an invaluable contribution to developments in almost all of the basic bio-medical sciences, including biochemistry, biophysics, physiology, pharmacology, genetics, imaging, and informatics. They also make a major contribution to behavioural and social sciences relevant to health research (psychology, sociology and economics). Their reach extends from the optimal design of laboratory experiments to the evaluation of public health interventions to prevent ill-health from the discovery, development and evaluation of new drugs and other more complex treatments to studies of the causes of disease, the evaluation of biomarkers to aid diagnosis (classification of disease), prognosis (prediction of disease outcome) and the evaluation of mechanisms of treatment action (treatment-effect mediation). Biostatistics is essentially a set of principles and methods for the design and analysis of research studies so that they are capable of answering the questions posed and of separating true effects from bias. It allows pattern and meaning to be extracted from often complex data sets, while at the same time allowing for the role of chance variation. The increase in computing power in the latter part of the 20th century has allowed a huge rise in the complexity and range of methods, making this an exciting and challenging area of work for those with the right skills. In the UK, there is a serious shortage of statisticians trained to Master?s level, which is the entry level to a broad range of employment sectors including the pharmaceutical industry, medical research and health services. The aim of this Master?s course is to equip students with the required knowledge to follow careers in these areas. The course is run jointly by statisticians in the School of Mathematics and in the School of Community Based Medicine at the University of Manchester, who have extensive experience of applied research in the bio-medical and health sciences. Therefore, the course provides unique opportunities for the students to gain knowledge and experience from a wide range of experts in both theoretical and applied biostatistics. The Advisory Board of the course are senior scientists working in the Pharmaceutical Industry (Pfizer, GSK and AstraZeneca), in research universities and the UK National Health Service (NHS), hence ensuring that the course stays in tune with the challenges that today?s biostatisticians face in their careers. The full-time course consists of course work and a dissertation over a period of one year (three semesters). The course work consists of eight units, four in each of semesters one and two. Unit 8 will consist of a number of smaller modules from which students must choose four according to their special interests. Part?time MSc students complete the course over 2 years with each unit examined at the end of the semester in which it is taken and the M.Sc dissertation prepared in the last 14 weeks of the second year. The course will give students knowledge and understanding of modern statistical methods. They will learn about their application in all areas of biological, behavioural and social sciences aimed at understanding and improving human health. The course will equip students with good knowledge of at least one commonly used statistical software packages (eg R, Stata). Opportunities are given to develop presentation and consultancy skills which are much valued by employers, as is evidence that students has undertaken their own piece of applied research. Semester I Course Units: 1. Introduction to Biostatistics 2. Linear and Generalised Linear Models 3. Epidemiology and Survival Analysis 4. Introduction to Clinical Trials Semester II Course Units: 5. Design and Analysis of Experiments 6. Longitudinal Data Analysis 7. Computationally Intensive Statistics 8. Advanced Topics in Biostatistics Semester III: Dissertation

Statistical methods make an invaluable contribution to developments in almost all of the basic bio-medical sciences, including biochemistry, biophysics, physiology, pharmacology, genetics, imaging, and informatics. They also make a major contribution to behavioural and social sciences relevant to health research (psychology, sociology and economics). Their reach extends from the optimal design of...

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