M9SICR Statistical Issues in Clinical Research

Faculty of Science
Autumn 2019
Extent and Intensity
2/0/0. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
Stephen Senn (lecturer), doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (deputy)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
A course for those who wish to understand the use of clinical trials and statistics in researching treatments. The value of statistics as a form of advanced common sense and a guide to rational decision-making will be stressed. The emphasis will be on practical philosophy rather than abstract algebra. Part one provides an overview of statistics in clinical research from several perspectives and is suitable for all who work in clinical research and have an interest in statistics. Amongst matters covered are the value of control, randomisation and blinding in clinical trials, sources of bias, including regression to the mean and some aspects of sample size determination. Part two is more technical and provides perspectives on the use of baselines, measurement of outcomes, design and analysis of more complex trials and the challenges of personalised medicine. The overall aim is to provide new slants on some familiar material as well as covering some more advanced topics, as a contribution towards improving discussion, debate and communication in clinical research.
Learning outcomes
The student will be able to understand the use of clinical trials and statistics in researching treatments. The value of statistics as a form of advanced common sense and a guide to rational decision-making will be stressed. The emphasis will be on practical philosophy rather than abstract algebra.
Syllabus
  • 1. History (As an excuse to introduce some topics)
  • 2. Philosophy (Causality and thinking statistically)
  • 3. Theory (An elementary explanation of the differences between Bayesians and frequentists)
  • 4. Basic Design (allocation to treatment and sample size determination)
  • 5. Baseline and Covariate Information (if, when and how to adjust)
  • 6. Measuring Treatment Effects (Transformations, dichotomies, intention to treat, missing data and multiplicity)
  • 7. Beyond the Single Centre Parallel Group Trial (Multi-centre trials, cross-over trials and cluster randomised trials)
  • 8. Beyond average effects? (Subgroups, personalised medicine, n-of.1 trials and real world data)
  • 9. The Rothamsted School (Lessons for big data and causal analysis)
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught only once.
The course is taught: in blocks.
Teacher's information
Block 1: Tuesday, 5 Nov 2019, 9:00-12:00 (Zasedačka ÚMS), 13:00-14:00 (Zasedačka ÚMS)

Block 2: Wednesday, 6 Nov 2019, 9:00-12:00 (Zasedačka ÚMS), 13:00-14:00 (Zasedačka ÚMS)

Block 3: Thursday, 7 Nov 2019, 9:00-12:00 (Zasedačka ÚMS), 13:00-14:00 (Zasedačka ÚMS)

  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2019/M9SICR