URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2AMS11&collegejaar=2025&taal=enOmschrijving
Introduction to statistical thinking and modelingSurvival analysis models: representation of survival time distributions and censoring
The maximum likelihood principle and how to deal with censored data
Non-parametric estimators: the empirical CDF and the Kaplan-Meier estimator
Beyond point estimation – uncertainty quantification
Bootstrapping approaches for uncertainty quantification
Statistical testing and p-values: application in the context of survival analysis
Diagnostic and Goodness-of-Fit tests
Regression models in the context of survival analysis
Assumed prior knowledge
Successful completion of a probability and statistics course within a data science, mathematics or engineering bachelor degree. A moderate level of maturity with abstract (mathematical) thinking.
Relevant probability theory topics: discrete and continuous random variables, independence, conditional probabilities and distributions, law of large numbers, central limit theorem.
Relevant statistics topics: basic descriptive statistics, the notion of a (frequentist) statistical model, estimators, confidence intervals and hypothesis testing.
Important calculus knowledge: integration and basic series results.
Doelstellingen
List the core principles of statistical modeling and the prototypical questions that can be answered by using statistical thinking.List statistical models and methodology relevant in survival analysis contexts
Explain and justify the relevance of the statistical models and methods for survival analysis based on empirical/heuristic considerations
Reflect critically on the applicability of survival analysis methodology in specific practical contexts
Apply survival analysis tools in practical contexts, and critically interpret the ensuing results