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Survival Analysis
Statistical Methods for Life Sciences
Preface
Introduction
1
Why do we care about probability?
2
Describing an uncertain observation
3
Describing outcomes with probability models
4
Repeating the sampling
5
Describing precision
6
Exercises
Appendix: discrete random variables
Appendix: cotinuous random variables
Statistical Inference
7
Introduction to hypothesis tests
8
Hypothesis testing using resampling
9
Parametric tests
10
Point and interval estimates
11
Rank-based tests
12
Multiple testing
13
Analysis of variance (ANOVA)
14
Exercises: Statistical inference
Linear regression
15
Introduction to linear models
16
Generalized linear models
17
Common cases
Exercises (introduction to linear models)
Exercises (model diagnostics)
Exercises (regression coefficients)
Mixed models
18
Simple linear regression
19
Mixed models
20
Mixed models (mathematical details)
21
Exercises: Mixed Effects Models
Survival Analysis
22
Introduction
23
Regression with survival response
24
Exercises: Survival analysis
Principal Component Analysis
25
Background
Exercises: Principal component analysis
Clustering
26
Clustering: art of finding groups
Exercises
From Linear Models to Machine Learning
27
From inference to prediction
28
Regularization
Exercises (Lasso)
References
Survival Analysis
Author
Olga Dethlefsen
21
Exercises: Mixed Effects Models
22
Introduction