Event History Analysis with R, Second Edition
Preface
Preface to the First Edition
1
Event History and Survival Data
1.1
Survival data
1.2
Right censoring
1.3
Left truncation
1.4
Time scales
1.4.1
The Lexis diagram
1.5
Event history data
1.6
More data sets
2
Single sample data
2.1
Continuous time model descriptions
2.1.1
The survival function
2.1.2
The density function
2.1.3
The hazard function
2.1.4
The cumulative hazard function
2.2
Discrete time models
2.3
Nonparametric estimators
2.3.1
The hazard atoms
2.3.2
The Nelson-Aalen estimator
2.3.3
The Kaplan-Meier estimator
2.4
Doing it in R
2.4.1
Nonparametric estimation
2.4.2
Parametric estimation
3
Cox Regression
3.1
Proportional Hazards
3.2
The Log-Rank Test
3.2.1
Several samples
3.3
Proportional Hazards Regression Models
3.3.1
Two Groups
3.3.2
Many Groups
3.3.3
The General Proportional Hazards Regression Model
3.4
Estimation of the Baseline Hazard
3.5
Explanatory Variables
3.5.1
Continuous Covariates
3.5.2
Factor Covariates
3.6
Interactions
3.6.1
Two factors
3.6.2
One factor and one continuous covariate
3.6.3
Two continuous covariates
3.7
Interpretation of parameter estimates
3.7.1
Continuous covariate
3.7.2
Factor
3.8
Proportional hazards in discrete time
3.8.1
Logistic regression
3.9
Model selection
3.9.1
Model selection in general
3.10
Doing it in
R
3.10.1
Likelihood Ratio Test
3.10.2
The estimated baseline cumulative hazard function
4
Poisson Regression
4.1
The Poisson Distribution
4.2
The connection to Cox regression
4.3
The connection to the piecewise constant hazards model
4.4
Tabular lifetime data
5
More on Cox Regression
5.1
Time-varying covariates
5.2
Communal covariates
5.3
Tied event times
6
Parametric Models
6.1
Proportional Hazards Models
6.1.1
The Weibull model
6.1.2
The Lognormal model
6.1.3
The Loglogistic model
6.1.4
The Extreme Value model
6.1.5
The Gompertz model
6.2
Comparing the Weibull and Lognormal fits
6.2.1
The piecewise constant hazards (pch) model
6.2.2
Testing the proportionality assumption with the Pch model
6.3
Choosing the best parametric model
6.3.1
Old age mortality
6.4
Accelerated Failure Time Models
6.4.1
The AFT regression model
6.4.2
AFT models in
R
6.5
Proportional hazards or AFT model?
6.6
Discrete time models
7
Register-Based Survival Data Models
8
Multivariate survival models
9
Causality and Mediation
9.1
Philosophical aspects of Causality
10
Competing risks models
Appendix
A
Basic statistical concepts
A.1
Statistical inference
A.1.1
Point estimation
A.1.2
Interval estimation
A.1.3
Hypothesis testing
A.2
Asymptotic theory
A.2.1
Partial likelihood
A.3
Model selection
A.3.1
Comparing nested models
A.3.2
Comparing non-nested models
B
Survival distributions
B.1
Relevant distributions in
R
B.2
Proportional hazards models
B.3
Accelerated failure time models
C
A brief introduction to
R
C.1
R in general
C.2
Some standard
R
functions
C.3
Writing functions
C.4
Graphics
C.5
Useful
R
functions
C.6
Help in
R
C.7
Functions for survival analysis
C.8
Reading data into
R
D
Survival packages in
R
References
Published with bookdown
Event History Analysis with R, Second Edition
9.1
Philosophical aspects of Causality