Daniels / Hogan Missing Data in Longitudinal Studies

Strategies for Bayesian Modeling and Sensitivity Analysis
Erscheinungsjahr 2008
ISBN: 978-1-4200-1118-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 328 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Drawing from the authors’ own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues. The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness. With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

Zielgruppe

Researchers, applied scientists, and graduate students in statistics, biostatistics, epidemiology, economics, sociology, and public health; government agencies and industry professionals concerned with regulatory decision making based on clinical trials.

Autoren/Hrsg.

Daniels, Michael J.

Hogan, Joseph W.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

PREFACE
Description of Motivating Examples
Overview
Dose-Finding Trial of an Experimental Treatment for Schizophrenia
Clinical Trial of Recombinant Human Growth Hormone (rhGH) for Increasing Muscle Strength in the Elderly
Clinical Trials of Exercise as an Aid to Smoking Cessation in Women: The Commit to Quit Studies
Natural History of HIV Infection in Women: HIV Epidemiology Research Study (HERS) Cohort
Clinical Trial of Smoking Cessation among Substance Abusers: OASIS Study
Equivalence Trial of Competing Doses of AZT in HIV-Infected Children: Protocol 128 of the AIDS Clinical Trials Group
Regression Models
Overview
Preliminaries
Generalized Linear Models
Conditionally Specified Models
Directly Specified (Marginal) Models
Semiparametric Regression
Interpreting Covariate Effects
Further Reading
Methods of Bayesian Inference
Overview
Likelihood and Posterior Distribution
Prior Distributions
Computation of the Posterior Distribution
Model Comparisons and Assessing Model Fit
Nonparametric Bayes
Further Reading
Bayesian Analysis using Data on Completers
Overview
Model Selection and Inference with a Multivariate Normal Model: Analysis of the Growth Hormone Clinical Study
Inference with a Normal Random Effects Model: Analysis of the Schizophrenia Clinical Trial
Model Selection and Inference for Binary Longitudinal Data: Analysis of CTQ I
Summary
Missing Data Mechanisms and Longitudinal Data
Introduction
Full vs. Observed Data
Full-Data Models and Missing Data Mechanisms
Assumptions about Missing Data Mechanism
Missing at Random Applied to Dropout Processes
Observed-Data Posterior of Full-Data Parameters
The Ignorability Assumption
Examples of Full-Data Models under MAR
Full-Data Models under MNAR
Summary
Further Reading
Inference about Full-Data Parameters under Ignorability
Overview
General Issues in Model Specification
Posterior Sampling Using Data Augmentation
Covariance Structures for Univariate Longitudinal Processes
Covariate-Dependent Covariance Structures
Multivariate Processes
Model Comparisons and Assessing Model Fit with Incomplete Data under Ignorability
Further Reading
Case Studies: Ignorable Missingness
Overview
Analysis of the Growth Hormone Study under MAR
Analysis of the Schizophrenia Clinical Trial under MAR Using Random Effects Models
Analysis of CTQ I Using Marginalized Transition Models under MAR
Analysis of Weekly Smoking Outcomes in CTQ II Using Auxiliary Variable MAR
Analysis of HERS CD4 Data under Ignorability Using Bayesian p-Spline Models
Summary
Models for handling Nonignorable Missingness
Overview
Extrapolation Factorization
Selection Models
Mixture Models
Shared Parameter Models
Model Comparisons and Assessing Model Fit in Nonignorable Models
Further Reading
Informative Priors and Sensitivity Analysis
Overview
Some Principles
Parameterizing the Full-Data Model
Pattern-Mixture Models
Selection Models
Elicitation of Expert Opinion, Construction of Informative Priors, and Formulation of Sensitivity Analyses
A Note on Sensitivity Analysis in Fully Parametric Models
Literature on Local Sensitivity
Further Reading
Case Studies: Model Specification and Data Analysis under Missing Not at Random
Overview
Analysis of Growth Hormone Study Using Pattern-Mixture Models
Analysis of OASIS Study Using Selection and Pattern-Mixture Models
Analysis of Pediatric AIDS Trial Using Mixture of Varying Coefficient Models
Appendix: distributions
Bibliography
Index