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Joint Models for Longitudinal and Time-To-Event Data With Applications in R

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ISBN-10: 1439872864

ISBN-13: 9781439872864

Edition: 2012

Authors: Dimitris Rizopoulos

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In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. This book provides a full treatment of joint models (JM) for longitudinal and time-to-event data. The content will be explanatory rather than mathematically rigorous and applications will be emphasized. All illustrations put forward will be available in the R programming language via the freely available package JM written by the author.
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Book details

Copyright year: 2012
Publisher: Taylor & Francis Group
Publication date: 7/9/2012
Binding: Paperback
Pages: 275
Size: 6.46" wide x 9.33" long x 0.79" tall
Weight: 1.408
Language: English

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