A5: Introduction to Statistics and Data Analysis
The module introduces important concepts and approaches in descriptive and inferential statistics as they are relevant in drug discovery & development as well as therapeutic use. The overall aim of the module is to understand the theoretical concepts and its underlying assumptions of the different statistical approaches used in pharmacometrics.
|8:45 – 10:45||Introduction to statistics and key concepts in probability||Estimation: point estimators, confidence intervals||Non-linear regression, two-stage approach||Non-linear mixed effect (NLME) approach||Bayesian statistics|
|30 min||Coffee break|
|11:15 - 13:00||Introduction to R and first hands-on with R||Hands-on with R||Hands-on with R||Hands-on with R||Hands-on with R|
|60 min||Lunch break|
|14:00 - 16:00||Descriptive and inferential statistics||Hypothesis testing||Guest talk on statistics in industry||Bootstrap, data partitioning||Monte Carlo approaches, summary, feedback & closing|
|15 min||Coffee break|
|16:15 - 17:45||Hands-on with R||Hands-on with R||Social evening||Hands-on with R|
|Evening||Social Event (most likely on Wednesday)|
- Prof. Wilhelm Huisinga; theoretical lectures
- PharMetrX PhD students (2nd/3rd year); hands-on exercises
- External contributions by our faculty members
Hard- and software
- Please bring your own laptop.
- The practical exercises will be in R. Please download the latest version here. You might want to download as well RStudio, which gives you a powerful GUI to R.
- W. Huisinga, Handouts of slides.
- Kyle Siegrist, Random (The Virtual Laboratory in Probability and Statistics), online resource @ University of Alabama in Huntsville. A very nice useful online resource with many applets.
- Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, 2004 (2nd corrected printing)
- Further references will be provided during the course.