2  Schedule

Please stay updated on Moodle — if a lecture may be canceled or moved, or if more material is uploaded by some .

See reading material at Moodle or in the specific sessions. All reading material is collected in Chapter 1.

Session #	Date	Topic	Notes
1	28/9	Install R and RStudio, basic functions, and data types	Install programs before! see Pre-course tasks. In general, go through all the Pre-course tasks, so that you can follow along without technical issues slowing you down
2	6/10	Probability and stochastic variables, distributions and realizations
3	9/10	Outcome types and measuring effects
4	13/10	Descriptive data: box, violin, and bar plots
5	15/10	Law of Large Numbers, uncertainty, permutation tests, bootstrapping
6	27/10	Data manipulation
7	29/10	Workshop: Operationalizing a research question (SAP examples)	NB: We will work on tasks relevant for the course assignment
8	2/11	Systematic vs. random noise, hypothesis testing, reproducibility, confounding, selection bias
9	4/11	Linear regression and likelihood theory (estimand, estimator, estimate)
10	5/11	Logistic regression and likelihood theory
11	10/11	Interaction and polynomials
12	11/11	Poisson regression
13	13/11	Time-to-event analysis
14	16/11	ANOVA, repeated measures, difference-in-difference
15	17/11	Margins analysis and margins plots
16	20/11	Power calculations
17	23/11	Workshop: Prediction vs. explanatory models (diagnostics)	NB: We will work on tasks relevant for the course assignment
18	26/11	Validation of binary prediction models (power, calibration, ROC)
19	27/11	Validation of continuous prediction models (including power calculations)
20	30/11	Workshop: From statistical analysis to clinical decision-making	NB: We will work on tasks relevant for the course assignment
21	1/12	Introduction to meta-analysis