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.
Frequency: Every other year in September/October.
Module in 2022: 29 Aug – 02 Sep 2022, University of Potsdam/Germany. The schedule below is generic. For each PhD student year, the specific schedule will be sent via email to the participants.
|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
- Dr. Niklas Hartung; 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.
- Detailed references will be provided during the course.
- 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)