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