Joint Modeling of Longitudinal and Survival Data with R

ISBN-13: 9781439872239 / Angielski / Twarda / 2023 / 200 str.

Peter Philipson

Spis treści

Introduction and Background
Survival data
Longitudinal data

Motivations for Joint Modeling
Inspecting and visualizing the data
Historical development
Joint modeling

Random Effects Models I: Intercept and Slope Models
Estimation procedure
Fitting a random intercept joint model in R: CD4 cell count data
Interpreting the random effects joint model and comparison with separate model

Random Effects Models II: Flexible Latent Association Models
Joint modeling with different random effects structures
Fitting extended joint models in R: CD4 cell count and liver cirrhosis data

Transformation Model
Estimation procedure
Fitting the transformation model in R: schizophrenia trial data
Which sort of joint model should I choose?

Multiple Endpoints: Joint Modeling for Competing Risks
Extending the joint model to multiple outcomes

Joint Modeling of Longitudinal Data with Discrete Time Dropout
Diggle-Kenward model
Random effects approach
Implementation in R: schizophrenia trial data revisited
Alternative approaches and comparison

References

Function Index