This is an introductory but intensive programme on the practical application of statistical methods in clinical research. The sessions are based on using examples from the scientific research literature.
This programme is particularly relevant to the following groups:
- All research students (particularly before starting the project or at the end when analysing and interpreting data)
- Students with little/ no knowledge of medical statistics or those who wish to refresh their memory
Key themes are:
- Understanding the different types of data
- Summarising each type of data
- Checking and displaying data
- Making comparisons, and correlations
- Statistical significance
- Problems with small studies
- Regression analyses
- Considering one factor, or multiple factors at the same time
- Diagnostic/prognostic tests
- Interpreting results, and formulating conclusions
A major feature of this course is that there is no algebra or presentation of the mechanics of the statistical methods used, which students say has been a hindrance to understanding medical statistics when taught elsewhere. All analyses should be now done with statistical packages, without the need to understand in detail how the methods work. Our goal is to concentrate on knowing when to use the most appropriate statistical method for each analysis, but importantly, properly understanding the output. Terms such as confidence intervals and p-values are presented using simple language.
All the sessions use examples from the literature, based on a variety of topics from the different faculties (including human and laboratory studies), and are interactive.
The course is run over 4.5 days (two weeks). Exercises are provided at the end of each day, and are discussed by the tutors in the following session. At the end of the last day there will be an opportunity for students to ask questions about their own project in relation to the topics covered during the course, with comments from the tutor and other students.