Event history analysis with R [2nd ed.]
By: Brostrom, Goran.
Series: The R Series. Publisher: Boca Raton CRC Press 2022Edition: 2nd ed.Description: xxxv, 304p.ISBN: 9781138587717.Subject(s): R (Computer program language) | Event history analysis | Social sciences -- Statistical methodsDDC classification: 304.6 | B794e2 Summary: 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.| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Books
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PK Kelkar Library, IIT Kanpur | General Stacks | 304.6 B794e2 (Browse shelf) | Checked out to Aditya Medury (E0603500) | 01/01/2024 | A185525 |
A Chapman and Hall Book
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.
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