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E-raamat: Event History Analysis with R

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With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.

Features

  • Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression
  • Presents mathematical details as well as technical material in an appendix
  • Includes real examples with applications in demography, econometrics, and epidemiology
  • Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics

A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.

Arvustused

"This book in The R Series from Chapman & Hall acts much as a companion to the R package eha by the same author. If one wants to analyse such data using R, then the book is well worthwhile. Although it is written more from the point of view of a reader comfortable in using R [ and] wanting to learn more about demographic data, it also offers something for the demographer looking to extend the scope of their analyses. the depth of treatment is about right to form the core of a lecture course " Mark Bebbington, Australian & New Zealand Journal of Statistics, 2013

List of Figures
ix
List of Tables
xiii
Preface xv
1 Event History and Survival Data
1(16)
1.1 Introduction
1(1)
1.2 Survival Data
1(4)
1.3 Right Censoring
5(1)
1.4 Left Truncation
6(1)
1.5 Time Scales
7(2)
1.6 Event History Data
9(2)
1.7 More Data Sets
11(6)
2 Single Sample Data
17(14)
2.1 Introduction
17(1)
2.2 Continuous Time Model Descriptions
17(5)
2.3 Discrete Time Models
22(1)
2.4 Nonparametric Estimators
23(4)
2.5 Doing it in R
27(4)
3 Cox Regression
31(26)
3.1 Introduction
31(1)
3.2 Proportional Hazards
31(2)
3.3 The Log-Rank Test
33(6)
3.4 Proportional Hazards in Continuous Time
39(3)
3.5 Estimation of the Baseline Hazard
42(1)
3.6 Explanatory Variables
42(2)
3.7 Interactions
44(3)
3.8 Interpretation of Parameter Estimates
47(1)
3.9 Proportional Hazards in Discrete Time
47(1)
3.10 Model Selection
48(1)
3.11 Male Mortality
49(8)
4 Poisson Regression
57(10)
4.1 Introduction
57(1)
4.2 The Poisson Distribution
57(2)
4.3 The Connection to Cox Regression
59(3)
4.4 The Connection to the Piecewise Constant Hazards Model
62(1)
4.5 Tabular Lifetime Data
62(5)
5 More on Cox Regression
67(18)
5.1 Introduction
67(1)
5.2 Time-Varying Covariates
67(1)
5.3 Communal covariates
68(3)
5.4 Tied Event Times
71(3)
5.5 Stratification
74(1)
5.6 Sampling of Risk Sets
75(2)
5.7 Residuals
77(3)
5.8 Checking Model Assumptions
80(3)
5.9 Fixed Study Period Survival
83(1)
5.10 Left- or Right-Censored Data
84(1)
6 Parametric Models
85(42)
6.1 Introduction
85(1)
6.2 Proportional Hazards Models
85(27)
6.3 Accelerated Failure Time Models
112(3)
6.4 Proportional Hazards or AFT Model?
115(1)
6.5 Discrete Time Models
116(11)
7 Multivariate Survival Models
127(12)
7.1 Introduction
127(2)
7.2 Frailty Models
129(5)
7.3 Parametric Frailty Models
134(2)
7.4 Stratification
136(3)
8 Competing Risks Models
139(8)
8.1 Introduction
139(1)
8.2 Some Mathematics
140(1)
8.3 Estimation
140(1)
8.4 Meaningful Probabilities
140(1)
8.5 Regression
141(3)
8.6 R Code for Competing Risks
144(3)
9 Causality and Matching
147(12)
9.1 Introduction
147(1)
9.2 Philosophical Aspects of Causality
147(1)
9.3 Causal Inference
148(2)
9.4 Aalen's Additive Hazards Model
150(2)
9.5 Dynamic Path Analysis
152(1)
9.6 Matching
153(4)
9.7 Conclusion
157(2)
A Basic Statistical Concepts
159(6)
A.1 Introduction
159(1)
A.2 Statistical Inference
159(2)
A.3 Asymptotic theory
161(2)
A.4 Model Selection
163(2)
B Survival Distributions
165(12)
B.1 Introduction
165(1)
B.2 Relevant Distributions in R
165(7)
B.3 Parametric Proportional Hazards and Accelerated Failure Time Models
172(5)
C A Brief Introduction to R
177(28)
C.1 R in General
177(5)
C.2 Some Standard R Functions
182(5)
C.3 Writing Functions
187(6)
C.4 Graphics
193(1)
C.5 Probability Functions
194(3)
C.6 Help in R
197(1)
C.7 Functions in eha and survival
197(5)
C.8 Reading Data into R
202(3)
D Survival Packages in R
205(4)
D.1 Introduction
205(1)
D.2 eha
205(1)
D.3 survival
206(1)
D.4 Other Packages
207(2)
Bibliography 209(4)
Index 213
Göran Broström is a professor emeritus of statistics in the Centre for Population Studies at Umeå University in Sweden.
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