2  Schedule

Warning

🚧 This section is being actively worked on. 🚧

The course is structured as a series of participatory live-coding sessions interspersed with hands-on exercises and group work, using either a practice dataset or some other real-world dataset. There are some lectures given, mainly at the start and end of the course. The general schedule outline is shown in the below table. This is not a fixed schedule of the timings of each session—some may be shorter and others may be longer. Instead, it is meant to be an approximate guide and overview.

Week	Date	Topic	Notes/Readings
1	xx	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 issue slowing you down
2	xx	Probability and stochastic variables, distributions and realizations
3	xx	Outcome types and measuring effects
4	xx	Descriptive data: box, violin, and bar plots
5	xx	course: Operationalizing a research question (SAP examples)	Group exercises
6	xx	Law of Large Numbers, uncertainty, permutation tests, bootstrapping
7	xx	Systematic vs. random noise, hypothesis testing, reproducibility, confounding, selection bias
8	xx	Data manipulation
9	xx	Linear regression and likelihood theory (estimand, estimator, estimate)
10	xx	Logistic regression and likelihood theory
11	xx	Scatter plots, best fit, and interaction
12	xx	Generalized linear regression
13	xx	Time-to-event analysis
14	xx	ANOVA, repeated measures, difference-in-difference
15	xx	Margins analysis and margins plots
16	xx	Power calculations
17	xx	course: Prediction vs. explanatory models (diagnostics)
18	xx	Validation of binary prediction models (power, calibration, ROC)
19	xx	Validation of continuous prediction models (including power calculations)
20	xx	course: From statistical analysis to clinical decision-making
21	xx	Introduction to meta-analysis