Design and Analysis of Experiments

The purpose of the course is to introduce a general statistical approach to the design of laboratory and similar experiments and to analyse the resulting data, including statistical quality control.

The statistical and methodological content of the course is illustrated with a number of concrete and frequently used applications within designed experiments. Examples are crossover and repeated measures designs, toxicity testing, bio-equivalence analyses, simulation models and adaptive experimentation.

The following topics will be covered

• Introduction to statistical modeling, hypothesis testing and other relevant methods within data science
• Principles of confounding, randomization, blocking and balancing
• Commonly used experimental plans in pharmaceutical sciences
• Estimation of necessary sample size for experimental designs
• Estimation, testing and interpretation of treatment effects
• Designing experiments with many factors
• Designing experiments with restrictions on randomization and repeated measures designs
• Adapive experimentation including optimization using response surface methodology
• Principles of statistical quality control
• Principles of experimental designs for large pharmaceutical simulation models with many variables

The statistical models and experimental designs are analysed, designed and illustrated using the statistical software R.