Multistate Models for the Analysis of Life History Data: 1st Edition (Hardback) book cover

Multistate Models for the Analysis of Life History Data

1st Edition

By Richard J Cook, Jerald F. Lawless

Chapman and Hall/CRC

440 pages

Purchasing Options:$ = USD
Hardback: 9781498715607
pub: 2018-05-04
$99.95
x
eBook (VitalSource) : 9781315119731
pub: 2018-05-15
from $49.98


FREE Standard Shipping!

Description

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research.

Features

  • Discusses a wide range of applications of multistate models
  • Presents methods for both continuously and intermittently observed life history processes
  • Gives a thorough discussion of conditionally independent censoring and observation processes
  • Discusses models with random effects and joint models for two or more multistate processes
  • Discusses and illustrates software for multistate analysis that is available in R
  • Target audience includes those engaged in research and applications involving multistate models

Richard Cook is Canada Research Chair in Statistical Methods for Health Research at the University of Waterloo. He has received the Gold Medal of the Statistical Society of Canada and is a Fellow of the American Statistical Association. He collaborates and consults widely on health research and has given many short courses. He and Dr. Lawless previously coauthored the influential book, The Statistical Analysis of Recurrent Events (Springer, 2007).

Jerald Lawless is Distinguished Professor Emeritus at the University of Waterloo. He is a Fellow of the Royal Society of Canada, a Gold Medal recipient of the Statistical Society of Canada and Fellow of the American Statistical Association. He is a past editor of Technometrics and has collaborated and consulted in numerous areas. He has presented many short courses, with Dr. Cook and individually.

"The authors of the book are internationally renowned experts in the field of multi-state modeling and have written an extremely clear and comprehensive book on the topic that covers many different aspects, from the fundamental theory to the practical side of analyzing data and interpreting results. The examples are well chosen to represent the most common types of multi-state processes that public health researchers could encounter. The inclusion of software code to illustrate how the models can be fit and interpreted is especially helpful to readers." (Mimi Kim, Albert Einstein College of Medicine)

Reviews

"The authors of the book are internationally renowned experts in the field of multi-state modeling and have written an extremely clear and comprehensive book on the topic that covers many different aspects, from the fundamental theory to the practical side of analyzing data and interpreting results. The examples are well chosen to represent the most common types of multi-state processes that public health researchers could encounter. The inclusion of software code to illustrate how the models can be fit and interpreted is especially helpful to readers."

~Mimi Kim, Albert Einstein College of Medicine

"This is a very nice book that does not exist on the market yet, and the multistate models for example are not well covered in terms of text books. We here have a book that really takes the multistate aspect seriously and provides many genuine examples that are discussed in depth. I cannot recall seeing examples in such depth in other books that deal with similar topics. This is not easy to do but the authors succeed in this fully."

~Thomas Scheike, University of Copenhagen

"This book includes a wide-ranging review of the use of multistate models for the analysis of longitudinal data arising from healthcare. The presentation is very clear and strikes a good balance between general description and rigorous specification of appropriate statistical models, including assumptions and limitations. Illustration of the methods using some substantive datasets, based on the authors’ experience of monitoring complex diseases, provides further insight into the value of different approaches. Both the level of detail and the pace of the arguments is very good."

~Linda Sharples, London School of Hygiene and Tropical Medicine

Table of Contents

Preface

List of Figures

List of Tables

Glossary

Abbreviations

  1. Introduction to Life History Processes and Multistate Models
  2. Life History Analysis with Multistate Models

    Some Illustrative Studies

    Disease Recurrence Following Treatment in a Clinical Trial

    Complications from Type Diabetes

    Joint Damage in Psoriatic Arthritis

    Viral Load Dynamics in Individuals with HIV Infection

    Introduction to Multistate Processes

    Counting Processes and Multistate Models

    Features of Multistate Processes

    Marginal Features and Partial Models

    Some Aspects of Modeling, Analysis and Design

    Objectives

    Components of a Model

    Study Design and Data

    Software

    Introduction to Some Studies and Dataframes

    A Trial of Breast Cancer Patients with Skeletal Metastases

    An International Breast Cancer Trial

    Viral Rebounds in HIV-positive Individuals 0

    Viral Shedding in HIV Patients with CMV Infection 0

    Bibliographic Notes

    Problems

     

  3. Event History Processes and Multistate Models
  4. Intensity Functions and Counting Processes

    Likelihood for Multistate Analyses

    Product Integration and Sample Path Probabilities

    Time-Dependent Covariates and Random Censoring

    Some Important Multistate Models

    Modulated Markov Models

    Modulated Semi-Markov Models

    Models with Dual Time Scales

    Models Accommodating Heterogeneity

    Linked Models and Local Dependence

    Process Features of Interest

    Simulation of Multistate Processes

    Bibliographic Notes

    Problems

     

  5. Multistate Analysis Based on Continuous Observation
  6. Maximum Likelihood Methods for Parametric Models

    Markov Models

    Semi-Markov Models

    Multistate Processes with Hybrid Time Scales

    Comments on Parametric Models

    Nonparametric Estimation

    Markov Models

    An Illness-death Analysis of a Metastatic Breast Cancer Trial 0

    Semi-Markov Models

    Recurrent Outbreaks of Symptoms from Herpes Simplex

    Virus

    Semiparametric Regression Models

    Multiplicative Modulated Markov Models

    Regression Analysis of a Palliative Breast Cancer Trial

    Multiplicative Modulated Semi-Markov Models

    Regression Analysis of Outbreaks from Herpes Simplex Virus

    Additive Markov and Semi-Markov Models

    Herpes Data Analyses with Additive Model

    Nonparametric Estimation of State Occupancy Probabilities

    Aalen-Johansen Estimates

    Adjustment for Process-Dependent Censoring

    Skeletal Complications and Mortality in Cancer Metastatic

    to Bone

    Model Assessment 0

    Checking Parametric Models 0

    Semiparametric Models 0

    Predictive Performance of Models 0

    Consequences of Model Misspecification and Robustness 0

    Design Issues 0

    Bibliographic Notes

    Problems

     

  7. Some examples of analysis with multistate models
  8. Competing Risks Analysis

    Model Features and Intensity-based Analysis

    Methods Based on Cumulative Incidence Functions

    Methods Based on Direct Binomial Regression

    Models for State Occupancy Based on Pseudo-Values

    A Competing Risks Analysis of Shunts in Hydrocephalus

    Alternative Methods for State Occupancy Probabilities 0

    Estimation Based on State Entry Time Distributions 0

    Estimation Based on Binomial Data

    A Utility-based Analysis of a Therapeutic Breast Cancer

    Clinical Trial

    Analysis of State Sojourn Time Distributions

    Bibliographic Notes

    Problems 0

     

  9. Studies with Intermittent Observation of Individuals
  10. Introduction

    Estimation and Analysis for Markov Models

    Model Fitting

    Parametric Information and Study Design

    Model Checking 0

    Illustration: Progression of Diabetic Retinopathy

    Nonparametric Estimation of State Occupancy Probabilities 0

    Process-Dependent Observation Times

    Further Remarks on Dependent Visit Processes

    Marginal Features and Inverse-Intensity of Visit Weighting

    Estimation of Visit Process Intensities

    Nonparametric Estimation of Occupancy Probabilities

    Progression to Mutilans Arthritis

    Intermittent Observation and Non-Markov Models

    Mixed Observation Schemes

    Illness-Death Models

    General Models

    Progression and Progression-Free Survival in Cancer Trials

    Bibliographic Notes

    Problems

     

  11. Heterogeneity and Dependence in Multistate Processes 0
  12. Accommodating Heterogeneity in Life History Processes 0

    Frailty Models in Survival Analysis 0

    A Progressive Multistate Model With Random Effects 0

    Random Effect Models with Recurrent States 0

    Analysis of Exacerbations in Chronic Bronchitis

    Modeling Correlated Multistate Processes

    Dependence Models Based on Random Effects

    Intensity-based Models for Local Dependence

    Dependence Models Retaining Simple Marginal Properties

    The Development of Axial Involvement in Psoriatic Arthritis

    Finite Mixture Models

    Notation, Likelihood Contribution and Estimation

    Modeling Variation in Disease Activity in Lupus 0

    Hidden Markov Models

    General Introduction

    A Hidden Markov Model for Retinopathy in the DCCT

    Bibliographic Notes

    Problems

  13. Process-dependent Sampling Schemes
  14. History and State-dependent Selection

    Types of Selection Schemes and Likelihoods

    Empirical Studies of Design Efficiencies for Markov Processes

    Prevalent Cohort Sampling and Failure Times

    Design Based on Probabilistic State-Dependent Sampling

    Selection and Initial Conditions with Heterogeneous Processes

    Initial Conditions with a Finite Mixture Model

    Outcome-Dependent Subsampling and Two-Phase Studies

    Introduction

    Multistate Processes

    Inference for Models with Semiparametric Multiplicative

    Intensities

    Design Issues

    Checks on Ignorable Follow-up Assumptions

    Bibliographic Notes

    Problems

     

  15. Additional Topics

Analysis of Process-Related Costs and Benefits

Individual-Level Models

Quality of Life Analysis and Breast Cancer Treatment

Individual-Level Decision Making

Population-Level Cost Analysis

Prediction

Viral Rebounds Among Persons with HIV

Joint Modeling of Marker and Event Processes 0

Roles of Markers in Disease Modeling 0

Models for Markers and Life History Processes 0

Intermittent Measurement of Markers and Censoring 0

A Joint Multistate and Discrete Marker Process Model 0

Remarks on Causal Inference with Life History Processes

Bibliographic Notes

Problems

 

Appendix A Selected Software Packages

A Software for Time to Event Data

A Parametric Analyses

A Semiparametric Analyses

A Selected Software for Multistate Analyses

A Multistate Software

A Methods based on Marginal Features

A Dataframe Construction with the mstate Package

A Software for Intermittently Observed Multistate Processes

A Miscellaneous Functions Useful for Multistate Analysis

A Timeline Plots

A Lexis Diagrams

A Drawing Multistate Diagrams with the Epi R Package

 

Appendix B Simulation of Multistate Processes

B Generating a Three-State Time-nonhomogeneous Markov Process

B Intensities Featuring Smooth Time Trends

B Processes with Piecewise-Constant Intensities

B Simulating Multistate Processes Under Intermittent Inspection

 

Appendix C Code and Output for Illustrative Analyses

C Illustrative Analysis of Diabetic Retinopathy

C Fitting the reversible Markov model MB with msm

C Fitting the progressive Markov model MB with msm

C Fitting the Hidden Markov Model with msm

C Code for the Onset of Arthritis Mutilans in PsA

C Dataframe and Fit of Intensity-based Model

C Marginal Model for Time to Entry to the Absorbing State

C Inverse Intensity Weighted Nonparametric Estimation

 

Appendix D Datasets

D Mechanical Ventilation in an Intensive Care Unit

D Outcomes in Blood and Marrow Transplantation (EBMT)

D A Trial of Platelet Dose and Bleeding Outcomes

D Shedding of Cytomegalovirus in HIV-Infected Individuals 0

D Micronutrient Powder and Infection in Malnurished Children

D The Dynamics of Giardia Lamblia Infection in Children

D The Development of Arthritis Mutilans in Psoriatic Arthritis

D Damage of the Sacroiliac (SI) Joints in Psoriatic Arthritis

D The Incidence of PsA in Individuals with Psoriasis

Bibliography

Index

About the Authors

Richard Cook is Canada Research Chair in Statistical Methods for Health Research at the University of Waterloo. He has received the Gold Medal of the Statistical Society of Canada and is a Fellow of the American Statistical Association. He collaborates and consults widely on health research and has given many short courses. He and Dr. Lawless previously coauthored the influential book, The Statistical Analysis of Recurrent Events (Springer, 2007).

Jerald Lawless is Distinguished Professor Emeritus at the University of Waterloo. He is a Fellow of the Royal Society of Canada, a Gold Medal recipient of the Statistical Society of Canada and Fellow of the American Statistical Association. He is a past editor of Technometrics and has collaborated and consulted in numerous areas. He has presented many short courses, with Dr. Cook and individually.

"The authors of the book are internationally renowned experts in the field of multi-state modeling and have written an extremely clear and comprehensive book on the topic that covers many different aspects, from the fundamental theory to the practical side of analyzing data and interpreting results. The examples are well chosen to represent the most common types of multi-state processes that public health researchers could encounter. The inclusion of software code to illustrate how the models can be fit and interpreted is especially helpful to readers." (Mimi Kim, Albert Einstein College of Medicine)

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General