Event History Analysis with R

 
 
CRC Press
  • 1. Auflage
  • |
  • erschienen am 3. September 2018
  • |
  • 236 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-315-36052-2 (ISBN)
 

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.

    1. Auflage
    • Englisch
    • London
    • |
    • Großbritannien
    Taylor & Francis Ltd
    • Für höhere Schule und Studium
    75 schwarz-weiße Abbildungen, 13 schwarz-weiße Tabellen
    • 8950,52 MB
    978-1-315-36052-2 (9781315360522)

    Göran Broström is a professor emeritus of statistics in the Centre for Population Studies at Umeå University in Sweden.

    Preface
    <strong>
    Event History and Survival Data
    </strong>Introduction
    Survival Data
    Right Censoring
    Left Truncation
    Time Scales
    Event History Data
    More Data Sets

    <strong>Single Sample Data
    </strong>Introduction
    Continuous Time Model Descriptions
    Discrete Time Models
    Nonparametric Estimators
    Doing it in R

    <strong>Cox Regression
    </strong>Introduction
    Proportional Hazards
    The Log-Rank Test
    Proportional Hazards in Continuous Time
    Estimation of the Baseline Hazard
    Explanatory Variables
    Interactions
    Interpretation of Parameter Estimates
    Proportional Hazards in Discrete Time
    Model Selection
    Male Mortality

    <strong>Poisson Regression
    </strong>Introduction
    The Poisson Distribution
    The Connection to Cox Regression
    The Connection to the Piecewise Constant Hazards Model
    Tabular Lifetime Data

    <strong>More on Cox Regression
    </strong>Introduction
    Time-Varying Covariates
    Communal covariates
    Tied Event Times
    Stratification
    Sampling of Risk Sets
    Residuals
    Checking Model Assumptions
    Fixed Study Period Survival
    Left- or Right-Censored Data

    <strong>Parametric Models
    </strong>Introduction
    Proportional Hazards Models
    Accelerated Failure Time Models
    Proportional Hazards or AFT Model?
    Discrete Time Models

    <strong>Multivariate Survival Models
    </strong>Introduction
    Frailty Models
    Parametric Frailty Models
    Stratification

    <strong>Competing Risks Models
    </strong>Introduction
    Some Mathematics
    Estimation
    Meaningful Probabilities
    Regression
    R Code for Competing Risks

    <strong>Causality and Matching
    </strong>Introduction
    Philosophical Aspects of Causality
    Causal Inference
    Aalen's Additive Hazards Model
    Dynamic Path Analysis
    Matching
    Conclusion

    <strong>Basic Statistical Concepts
    </strong>Introduction
    Statistical Inference
    Asymptotic theory
    Model Selection

    <strong>Survival Distributions
    </strong>Introduction
    Relevant Distributions in R
    Parametric Proportional Hazards and Accelerated Failure Time Models

    <strong>A Brief Introduction to R
    </strong>R in General
    Some Standard R Functions
    Writing Functions
    Graphics
    Probability Functions
    Help in R
    Functions in eha and survival
    Reading Data into R

    <strong>Survival Packages in R
    </strong>Introduction
    eha
    survival
    Other Packages

    Bibliography
    Index

    "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
     

    "This book in <i>The R Series</i> 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, <em>Australian & New Zealand Journal of Statistics</em>, 2013

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