Daniel Stekhoven: Catalogue data in Autumn Semester 2019 |
| Name | Dr. Daniel Stekhoven |
| Address | NEXUS Personalized Health Technol. ETH Zürich, SWS L 641 Wagistrasse 18 8952 Schlieren SWITZERLAND |
| Telephone | +41 44 632 21 61 |
| stekhoven@nexus.ethz.ch | |
| URL | http://www.nexus.ethz.ch |
| Department | Mathematics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-0683-00L | Statistics II  | 3 credits | 2V + 1U | D. Stekhoven | |
| Abstract | Extension of statistics for medical students. This lecture is based on the content of Statistics I. The focus will be on the understanding and the concrete application of statistical methods, as they are used in medical research. Exercises will be solved using the statistical programming environment R. | ||||
| Learning objective | After this course you will understand the concept of a broad selection of statistical methods (see also Content). Furthermore, you will know when to use which method. Especially, you will be able to read, understand, and scrutinise the results from such methods, whether these results are written or graphical. Using the statistical programming environment R, you will be able to read in data, analyse them in various ways, visualise and publish the results in reports or presentations. Knowing R will also enable you to reproduce published analyses, to check whether they work or to use them for your own medical research questions. | ||||
| Content | The course will cover the following topics. For the part on regression: simple linear regression; multiple regression (including factors and interactions); model selection; logistic regression (including odds ratio and their interpretation); mixed effects models; Bayes inference. For the part on data: categorical data (including univariate tests); power analysis (including a guide on writing an ethics proposal); dealing with missing values. For the part on further methods: supervised vs unsupervised learning; dimensional reduction (including PCA and tSNE); survival analysis (including Kaplan-Meier curves and logrank test). | ||||
| Lecture notes | There is no script. | ||||
| Literature | An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Springer, 2013; online available from the ETH Library | ||||
| Prerequisites / Notice | Required: Statistics I | ||||