Event History Analysis with R, 2nd Edition
- Length: 340 pages
- Edition: 2
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2021-11-11
- ISBN-10: 1138587710
- ISBN-13: 9781138587717
- Sales Rank: #0 (See Top 100 Books)
With an emphasis on social science applications, Event History Analysis with R, Second Edition, presents an introduction to survival and event history analysis using real-life examples. Since publication of the first edition, focus in the field has gradually shifted towards the analysis of large and complex datasets. This has led to new ways of tabulating and analysing tabulated data with the same precision and power as that of an analysis of the full data set. Tabulation also makes it possible to share sensitive data with others without violating integrity.
The new edition extends on the content of the first by both improving on already given methods and introducing new methods. There are two new chapters, Explanatory Variables and Regression, and Register- Based Survival Data Models. The book has been restructured to improve the flow, and there are significant updates to the computing in the supporting R package.
Features
- Introduction to survival and event history analysis and how to solve problems with incomplete data using Cox regression.
- Parametric proportional hazards models, including the Weibull, Exponential, Extreme Value, and Gompertz distributions.
- Parametric accelerated failure time models with the Lognormal, Loglogistic, Gompertz, Exponential, Extreme Value, and Weibull distributions.
- Proportional hazards models for occurrence/exposure data, useful with tabular and register based data, often with a huge amount of observed events.
- Special treatments of external communal covariates, selections from the Lexis diagram, and creating period as well as cohort statistics.
- “Weird bootstrap” sampling suitable for Cox regression with small to medium-sized data sets.
- Supported by an R package (https://CRAN.R-project.org/package=eha), including code and data for most examples in the book.
- A dedicated home page for the book at http://ehar.se/r/ehar2
This substantial update to this popular book remains an excellent resource for researchers and practitioners of applied event history analysis and survival analysis. It can be used as a text for a course for graduate students or for self-study.
Cover Half Title Title Page Copyright Page Contents List of Tables List of Figures Preface Preface to the First Edition Author 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. Proportional Hazards and Cox Regression 3.1. Proportional Hazards 3.2. The Log-Rank Test 3.2.1. Two samples 3.2.2. Several samples 3.3. Cox Regression Models 3.3.1. Two Groups 3.3.2. Many groups 3.3.3. The general Cox regression model 3.4. Estimation of the Baseline Cumulative Hazard Function 3.5. Proportional Hazards in Discrete Time 3.5.1. Logistic regression 3.6. Doing It in R 3.6.1. The estimated baseline cumulative hazard function 4. Explanatory Variables and Regression 4.1. Continuous Covariates 4.2. Factor Covariates 4.3. Interactions 4.3.1. Two factors 4.3.2. One factor and one continuous covariate 4.3.3. Two continuous covariates 4.4. Interpretation of Parameter Estimates 4.4.1. Continuous covariate 4.4.2. Factor 4.5. Model Selection 4.5.1. Model selection in general 5. Poisson Regression 5.1. The Poisson Distribution 5.2. The Connection to Cox Regression 5.3. The Connection to the Piecewise Constant Hazard 5.4. Tabular Lifetime Data 6. More on Cox Regression 6.1. Time-Varying Covariates 6.2. Communal Covariates 6.3. Tied Event Times 6.4. Stratification 6.5. Sampling of Risk Sets 6.6. Residuals 6.6.1. Martingale residuals 6.7. Checking Model Assumptions 6.7.1. Proportionality 6.7.2. Log-linearity 6.8. Fixed Study Period Survival 6.9. Left or Right Censored Data. 6.10. The Weird Bootstrap 7. Register-Based Survival Data Models 7.1. Tabular Data 7.2. Individual Data 7.3. Communal Covariates and Tabulation 7.3.1. Temperature and mortality, Umeå 1901–1950 7.3.2. Hot summers in Umeå, 1990–2014 8. Parametric Models 8.1. Proportional Hazards Models 8.1.1. The Weibull model 8.1.2. The Gompertz distribution 8.1.3. Application 8.1.4. The parametric model with left truncation 8.1.5. The piecewise constant proportional hazards model 8.1.6. Testing the proportional hazards assumption 8.1.7. Choosing the best parametric proportional hazards model 8.2. Accelerated Failure Time Models 8.2.1. The AFT regression model 8.2.2. AFT modeling in R 8.2.3. The Lognormal model 8.2.4. The Loglogistic model 8.2.5. The Gompertz model 8.3. Proportional Hazards or AFT Model? 8.4. Discrete Time Models 8.4.1. Data formats: wide and long 8.4.2. Binomial regression with glm 8.4.3. Survival analysis with coxreg 9. Multivariate Survival Models 9.1. An Introductory Example 9.2. Frailty Models 9.2.1. The simple frailty model 9.2.2. The shared frailty model 9.2.3. Parametric frailty models 9.3. Stratification 10. Causality and Matching 10.1. Philosophical Aspects on Causality 10.2. Causal Inference 10.2.1. Graphical models 10.2.2. Predictive causality 10.2.3. Counterfactuals 10.3. Aalen’s Additive Hazards Model 10.4. Dynamic Path Analysis 10.5. Matching 10.5.1. Paired data 10.5.2. More than one control 10.6. Conclusion 11. Competing Risks Models 11.1. Some Mathematics 11.2. Estimation 11.3. Meaningful Probabilities 11.4. Regression 11.5. R code for Competing Risks 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. Presentation of Results A.2.1. The project A.2.2. Tabular presentation A.2.3. Graphics A.3. Asymptotic Theory A.3.1. Partial likelihood A.4. Model Selection A.4.1. Comparing nested models A.4.2 Comparing non-nested models B. Survival Distributions B.1. Relevant Distributions in R B.1.1. The Exponential distribution B.1.2. The piecewise constant hazard distribution B.1.3. The Weibull distribution B.1.4. The Lognormal distribution B.1.5. The Loglogistic distribution B.1.6. The Gompertz distribution B.1.7. The Gompertz-Makeham distribution B.1.8. The Gamma distribution B.2. Proportional Hazards Models B.3. Accelerated Failure Time Models C. A Brief Introduction to R C.1. R in General C.1.1. R objects C.1.2. Expressions and assignments C.1.3. Objects C.1.4. Vectors and matrices C.1.5. Lists C.1.6. Data frames C.1.7. Factors C.1.8. Operators C.1.9. Recycling C.1.10. Precedence C.2. Some Standard R Functions C.2.1. Sequences C.2.2. Logical expression C.2.3. Indexing C.2.4. Vectors and matrices C.2.5. Conditional execution C.2.6. Loops C.2.7. Vectorizing C.3. Writing Functions C.3.1. Calling conventions C.3.2. The argument “…” C.3.4. Lazy evaluation C.3.5. Recursion C.3.6. Vectorized functions C.3.7. Scoping rules C.4. Standard Graphics C.5. Useful R Functions C.6. Help in R C.7. Functions for Survival Analysis C.7.1. Checking the integrity of survival data C.8. Reading Data into R C.8.1. Reading data from ASCII files C.8.2. Reading foreign data files D. Survival Packages in R D.1. eha D.2. survival D.3. Other Packages D.3.1. coxme D.3.2. timereg D.3.3. cmprsk Bibliography Index
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