Event history analysis with R. 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.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Vanegas, Luis Hernando; Paula, Gilberto A.: Log-symmetric regression models under the presence of non-informative left- or right-censored observations (2017)
- Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016)
- David Moriña; Albert Navarro: The R Package survsim for the Simulation of Simple and Complex Survival Data (2014)
- Sy Chiou; Sangwook Kang; Jun Yan: Fitting Accelerated Failure Time Models in Routine Survival Analysis with R Package aftgee (2014)
- Broström, Göran: Event history analysis with R (2012)
- Marco Munda; Federico Rotolo; Catherine Legrand: parfm: Parametric Frailty Models in R (2012)