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.
