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    Introducing Survival and Event History Analysis

    AvMelinda Mills

    Häftad, Engelska, 2010

    717 kr

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    Introducing Survival Analysis and Event History Analysis is an accessible, practical and comprehensive guide for researchers and students who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics.

     

    Engaging, easy to read, functional and packed with enlightening examples, 'hands-on' exercises and resources for both students and instructors, Introducing Survival Analysis and Event History Analysis allows researchers to quickly master these advanced statistical techniques. This book is written from the perspective of the 'user', making it suitable as both a self-learning tool and graduate-level textbook.

     

    Introducing Survival Analysis and Event History Analysis covers the most up-to-date innovations in the field, including advancements in the assessment of model fit, frailty and recurrent event models, discrete-time methods, competing and multistate models and sequence analysis. Practical instructions are also included, focusing on the statistical program R and Stata, enabling readers to replicate the examples described in the text.

     

    This book comes with a glossary, a range of practical and user-friendly examples, cases and exercises.

    Produktinformation

    • Utgivningsdatum:2010-12-21
    • Mått:170 x 242 x 17 mm
    • Vikt:510 g
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:300
    • Upplaga:1
    • Förlag:SAGE Publications
    • ISBN:9781848601024

    Utforska kategorier

    • Sociologi inom Samhälle och politik

    Recensioner i media

    This book is very useful for researchers and studentsin different scientific areas – social sciences and humanities, medicine, ingeneral every science where studies measuring time changes in variables areconducted...As the author explains, this book is written from theperspective of an absolute beginner – comprehensible and with a lot of examplesin the text, tables and graphs. It goes beyond an introductory textbook on thistopic, because it presents not only non-parametric models, semi-parametricmodels, parametric models, model-building and model diagnostics, but it is focused also on some more recent techniques like frailty and recurrent eventhistory models, discrete-time models, multistate models, competing riskanalysis and sequence analysis...Everyone who would like to start with Survival andEvent History analysis or to get more knowledge of Survival and Event Historyanalysis could do this by reading this bookStanislava Yordanova StoyanovaMethodspace

    Innehållsförteckning

    • The Fundamentals of Survival and Event History AnalysisIntroduction: What Is Survival and Event History Analysis?Key Concepts and TerminologyCensoring and TruncationMathematical Expression and Relation of Basic Statistical FunctionsHow Do the Survivor, Density and Hazard Function Relate?Why Use Survival and Event History Analysis?Overview of Survival and Event History ModelsExercisesUsing R and Other Computer Programs for Survival and Event History AnalysisIntroduction: Computer Programs for Survival and Event History AnalysisConducting Serious Data Analysis: Life LessonsWhy Use R?Downloading R on Your Personal ComputerAdd-On PackagesRunning RDetermining and Setting your Working DirectoryHelp and DocumentationImporting Data Into RWorking With Data: Opening and Accessing Variables from a Data FrameSaving Output as File, Workspace and History and Quitting RExercisesYour First Session: Using the Survival Package and Exploring Data Via Descriptive Statistics and GraphsYour First Session Using the ′Survival′ Package In FLoading and Examining the Survival Package and Rcmdrplugin.Survival Plug-InOpening and Examining DataThe Surv Object: Packaging the ′Survival Variable′Basic Descriptive StatisticsDescriptive Data Exploration with GraphsExercisesData and Data ReconstructionIntroduction: Why Discuss Data and Data Preparation?Sources of Event History DataSingle-Episode Data for Single Transition AnalysesMulti-Episode Data for Recurrent Event and Frailty AnalysesSubject-(Person)-Period Data for Discrete-Time Hazard ModelsThe Counting Process and Episode SplittingA Note on DatesExercisesNon-Parametric Methods: Estimating and Comparing Survival Curves Using the Kaplan-Meier EstimatorIntroductionThe Kaplan-Meier EstimatorProducing Kaplan-Meier EstimatesPlotting the Kaplan-Meier Survival CurveTesting Differences Between Two Groups Using SurvdiffStratifying the Analysis by a CovariateExercisesThe Cox Proportional-Hazards RegressionIntroduction: Why is The Cox Model So Popular?The Cox Regression ModelEstimating and Interpreting The Cox Model with Fixed CovariatesThe Cox Regression Model with Time-Varying CovariatesExercisesParametric ModelsIntroduction: What are Parametric Models and Why Use Them?Proportional Hazards (Ph) Versus Accelerated Failure Time (Aft) ModelsThe Path to Choosing a ModelEstimating and Interpreting Parametric Survival ModelsExponential and Piecewise Constant Exponential ModelWeibull ModelLog-Logistic and Log-Normal ModelsAdditional Parametric ModelsFinding the Best Fitting Parametric ModelExercisesModel Building and DiagnosticsIntroductionModel Building and Selection of CovariatesAssessing the Overall Goodness of Fit of Your ModelWhat is Residual Analysis?Testing Overall Model Adequacy: Cox-Snell ResidualsTesting the Proportional Hazards Assumption: Schoenfeld ResidualsChecking For Influential Observations: Score Residuals (Dfbeta Statistics)Assessing Nonlinearity: Martingale Residual and Component-Plus-Residual PlotsExercisesCorrelated and Discrete-Time Survival Data: Frailty, Recurrent Events and Discrete-Time ModelsIntroductionShared Frailty: Modeling Recurrent Events and Clustering In GroupsOther Frailty Models: Unshared, Nested, Joint and Additive ModelsEstimating Frailty Models in RExample of Frailty Model Estimation and InterpretationDiscrete-Time and Count ModelsExercisesMultiple Events and Entire Histories: Competing Risk, Multistate Models and Sequence AnalysisIntroductionCompeting Risk ModelsMultistate ModelsSequence Analysis: Modeling Entire HistoriesExercisesAppendix : Datasets Used in this Book