Notebook for Applied Survival Analysis using R
Preface
1
Introduction
1.1
What is Survival Analysis
1.2
What you need to know to use this book
1.3
Survival Data and Censoring
1.4
Some examples of survival data sets
2
Basic Principles of Survival Analysis
2.1
Hazard and Survival Functions
2.2
Other Representations of a Survival Distribution
2.3
Mean and Median Survival Time
2.4
Parametric Survival Distributions
2.5
Computing the Survival Function from the Hazard Function
2.6
A breif introduction to maximum likelihood estimation
3
Nonparametric Survival Curve Estimation
3.1
Nonparametric Estimation of the Survival Function
3.2
Finding the median survival and a confidence interval for the median
3.3
Median Follow-up Time
3.4
Obtaining a smoothed Hazard and Survival Function estimate
3.5
Left Truncation
4
Nonparametric Comparision of Survival Distributions
4.1
Comparing Two Groups of Survival Times
4.2
Stratified Tests
5
Regression Analysis Using the Proportional Hazards Model
5.1
Covariates and nonparametric Survival Models
5.2
Comparing Two Survival Distributions Using a Partial Likelihood Function
5.3
Partial Likelihood Hypothesis Tests
5.3.1
The Wald Test
5.3.2
The Score Test
5.3.3
The likelihood Ratio Test
5.4
The Partial Likelihood with Multiple Covariates
5.5
Estimating the Baseline Survival Function
5.6
Handling of Tied Survival Times
5.7
Left Truncation
6
Model Selection and Interpretation
6.1
Covariate Adjustment
6.2
Categorical and Continuous Covariates
6.3
Hypothesis Testing for Nested Models
6.4
The Akaike Information Criterion for Comparing Non-nested Models
6.5
Including Smooth Estimates of Continuous Covariates in a Survival Model
7
Model Diagnostics
7.1
Assessing Goodness of Fit Using Residuals
8
Time Dependent Covariates
9
Multiple Survival Outcomes and Competing Risks
10
Parametric Models
11
Sample Size Determination for Survival Studies
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7
Model Diagnostics
7.1
Assessing Goodness of Fit Using Residuals