The aim of the first part in this course is to describe various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates.
The second part of the course will focus on the analysis of contingency table data, where the cell entries represent counts that are cross-tabulated using categorical variables. Tests for (conditional) independence are discussed in the context of odds-ratios, relative risks and simple Chi²-tests. Upon successful completion of this courses, participants will be able to check test specific assumptions and will be familiar with common statistical methods in survival and categorical data analysis. The participants will have the ability to choose the suitable statistical analysis method for different data sets and will have insight how to interpret statistical results.
Survival analysis (Part 1): Overview of different Survival models, focussed on Kaplan Meier and Log Rank test, Cox Regression (time-dependent/independent)
Analyzing categorical data (Part 2): Odds Ratio, Relative-Risk, Chi², McNemar, estimation of cut-offs, ROC/AUC analysis, sensitivity and specifity, scientific presentation of results;
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