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| Preface | |
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| Introduction | |
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| Goals | |
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| Motivating Studies | |
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| Primary Biliary Cirrhosis Data | |
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| AIDS Data | |
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| Liver Cirrhosis Data | |
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| Aortic Valve Data | |
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| Other Applications | |
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| Inferential Objectives in Longitudinal Studies | |
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| Effect of Covariates on a Single Outcome | |
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| Association between Outcomes | |
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| Complex Hypothesis Testing | |
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| Prediction | |
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| Statistical Analysis with Implicit Outcomes | |
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| Overview | |
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| Longitudinal Data Analysis | |
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| Features of Longitudinal Data | |
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| Linear Mixed-Effects Models | |
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| Estimation | |
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| Implementation in R | |
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| Missing Data in Longitudinal Studies | |
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| Missing Data Mechanisms | |
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| Missing Not at Random Model Families | |
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| Further Reading | |
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| Analysis of Event Time Data | |
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| Features of Event Time Data | |
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| Basic Functions in Survival Analysis | |
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| Likelihood Construction for Censored Data | |
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| Relative Risk Regression Models | |
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| Implementation in R | |
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| Time-Dependent Covariates | |
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| Extended Cox Model | |
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| Further Reading | |
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| Joint Models for Longitudinal and Time-to-Event Data | |
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| The Basic Joint Model | |
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| The Survival Submodel | |
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| The Longitudinal Submodel | |
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| Joint Modeling in R: A Comparison with the Extended Cox Model | |
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| Estimation of Joint Models | |
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| Two-Stage Approaches | |
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| Joint Likelihood Formulation | |
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| Standard Errors with an Unspecified Baseline Risk Function | |
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| Optimization Control in JM | |
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| Numerical Integration | |
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| Numerical Integration Control in JM | |
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| Convergence Problems | |
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| Asymptotic Inference for Joint Models | |
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| Hypothesis Testing | |
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| Confidence Intervals | |
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| Design Considerations | |
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| Estimation of the Random Effects | |
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| Connection with the Missing Data Framework | |
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| Sensitivity Analysis under Joint Models | |
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| Extensions of the Standard Joint Model | |
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| Parameterizations | |
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| Interaction Effects | |
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| Lagged Effects | |
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| Time-Dependent Slopes Parameterization | |
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| Cumulative Effects Parameterization | |
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| Random-Effects Parameterization | |
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| Handling Exogenous Time-Dependent Covariates | |
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| Stratified Relative Risk Models | |
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| Latent Class Joint Models | |
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| Multiple Failure Times | |
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| Competing Risks | |
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| Recurrent Events | |
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| Accelerated Failure Time Models | |
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| Joint Models for Categorical Longitudinal Outcomes | |
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| The Generalized Linear Mixed Model (GLMM) | |
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| Combining Discrete Repeated Measures with Survival | |
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| Joint Models for Multiple Longitudinal Outcomes | |
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| Joint Model Diagnostics | |
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| Residuals for Joint Models | |
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| Residuals for the Longitudinal Part | |
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| Residuals for the Survival Part | |
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| Dropout and Residuals | |
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| Multiple Imputation Residuals | |
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| Fixed Visit Times | |
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| Random Visit Times | |
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| Random-Effects Distribution | |
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| Prediction and Accuracy in Joint Models | |
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| Dynamic Predictions of Survival Probabilities | |
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| Definition | |
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| Estimation | |
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| Implementation in R | |
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| Dynamic Predictions for the Longitudinal Outcome | |
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| Effect of Parameterization on Predictions | |
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| Prospective Accuracy for Joint Models | |
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| Discrimination Measures for Binary Outcomes | |
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| Discrimination Measures for Survival Outcomes | |
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| Prediction Rules for Longitudinal Markers | |
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| Discrimination Indices | |
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| Estimation under the Joint Modeling Framework | |
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| Implementation in R | |
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| A Brief Introduction to R | |
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| Obtaining and Installing R and R Packages | |
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| Simple Manipulations | |
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| Basic R Objects | |
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| Indexing | |
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| Import and Manipulate Data Frames | |
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| The Formula Interface | |
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| The EM Algorithm for Joint Models | |
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| A Short Description of the EM Algorithm | |
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| The E-step for Joint Models | |
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| The M-step for Joint Models | |
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| Structure of the JM Package | |
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| Methods for Standard Generic Functions | |
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| Additional Functions | |
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| References | |
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| Index | |