It is essential for graduate students to develop critical thinking in order to identify caveat in current research, develop new approaches to existing research problems and explore new horizon in their research fields. Students would find it very helpful, if real examples in clinical and epidemiological research are used to demonstrate how to achieve these goals, and this is the aim of this course. The syllabus of this course will loosely follow the order of chapters in my new book of the same title published by Chapman & Hall this summer with supplementary materials. Two tools to promote understanding of statistical modelling will be first introduced: vector geometry for linear models and the directed acyclic graphs for causal thinking. Then, background knowledge will be provided for students to identify the potential caveats and to use their previous knowledge to choose the most appropriate approaches or to develop new approaches. Potential scenarios and examples used in this course include:
• Testing the relation between baseline and changes
• The use of ratio variables in regression analysis
• Statistical methods for testing differences in changes in randomised controlled trials
• Lord’s paradox and the adjustment of baseline values in observational studies
• The problem of collinearity in linear models
• Testing statistical and biological interaction
• Confounding, causality and Simpson’s paradox
• Reversal paradox and the adjustment of intermediate variables on a causal pathway
• Testing direct, indirect and total effects
Students will be asked to give presentations to discuss the reading materials and lead discussion. Students’ performance will be assessed by their participation in the classroom discussion, their presentations and the final essays on one of the topics discussed in the course.