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

• 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

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

• 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

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

• 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