Multistate Models for the Analysis of Life History Data

Content: Cover
 Half title
 Editors
 Title
 Copyright
 Dedication
 Contents
 Preface
 Glossary
 Abbreviations
 Chapter 1 Introduction to Life History Processes and Multistate Models
 1.1 Life History Analysis with Multistate Models
 1.2 Some Illustrative Studies
 1.2.1 Disease Recurrence Following Treatment in a Clinical Trial
 1.2.2 Complications from Type 1 Diabetes
 1.2.3 Joint Damage in Psoriatic Arthritis
 1.2.4 Viral Load Dynamics in Individuals with HIV Infection
 1.3 Introduction to Multistate Processes
 1.3.1 Counting Processes and Multistate Models
 1.3.2 Features of Multistate Processes 1.3.3 Marginal Features and Partial Models1.4 Some Aspects of Modeling, Analysis and Design
 1.4.1 Objectives
 1.4.2 Components of a Model
 1.4.3 Study Design and Data
 1.5 Software
 1.6 Introduction to Some Studies and Dataframes
 1.6.1 A Trial of Breast Cancer Patients with Skeletal Metastases
 1.6.2 An International Breast Cancer Trial
 1.6.3 Viral Rebounds in HIV-Positive Individuals
 1.6.4 Viral Shedding in HIV Patients with CMV Infection
 1.7 Bibliographic Notes
 1.8 Problems
 Chapter 2 Event History Processes and Multistate Models
 2.1 Intensity Functions and Counting Processes 2.2 Likelihood for Multistate Analyses2.2.1 Product Integration and Sample Path Probabilities
 2.2.2 Time-Dependent Covariates and Random Censoring
 2.3 Some Important Multistate Models
 2.3.1 Modulated Markov Models
 2.3.2 Modulated Semi-Markov Models
 2.3.3 Models with Dual Time Scales
 2.3.4 Models Accommodating Heterogeneity
 2.3.5 Linked Models and Local Dependence
 2.4 Process Features of Interest
 2.5 Simulation of Multistate Processes
 2.6 Bibliographic Notes
 2.7 Problems
 Chapter 3 Multistate Analysis Based on Continuous Observation 3.1 Maximum Likelihood Methods for Parametric Models3.1.1 Markov Models
 3.1.2 Semi-Markov Models
 3.1.3 Multistate Processes with Hybrid Time Scales
 3.1.4 Comments on Parametric Models
 3.2 Nonparametric Estimation
 3.2.1 Markov Models
 3.2.2 An Illness-Death Analysis of a Metastatic Breast Cancer Trial
 3.2.3 Semi-Markov Models
 3.2.4 Recurrent Outbreaks of Symptoms from Herpes Simplex Virus
 3.3 Semiparametric Regression Models
 3.3.1 Multiplicative Modulated Markov Models
 3.3.2 Regression Analysis of a Palliative Breast Cancer Trial
 3.3.3 Multiplicative Modulated Semi-Markov Models 3.3.4 Regression Analysis of Outbreaks from Herpes Simplex Virus3.3.5 Additive Markov and Semi-Markov Models
 3.3.6 Analysis of Herpes Outbreaks with Additive Models
 3.4 Nonparametric Estimation of State Occupancy Probabilities
 3.4.1 Aalen-Johansen Estimates
 3.4.2 Adjustment for Process-Dependent Censoring
 3.4.3 Skeletal Complications and Mortality in Cancer Metastatic to Bone
 3.5 Model Assessment
 3.5.1 Checking Parametric Models
 3.5.2 Semiparametric Models
 3.5.3 Predictive Performance of Models
 3.5.4 Consequences of Model Misspecification and Robustness
 3.6 Design Issues