Handbook of Missing Data Methodology

Handbook of Missing Data Methodology

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ISBN 9781439854617
Cat# K12536

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Features

  • Provides a comprehensive and up-to-date summary of many methodological advances and the latest applications of missing data methods in empirical research
  • Describes major developments from the extensive statistical literature on parametric and semi-parametric models with missing data
  • Highlights the importance of sensitivity analysis
  • Explains how to manage missing data in clinical trials and surveys

Summary

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.

Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.

The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters.

Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

Table of Contents

Preliminaries
Introduction and Preliminaries Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis

Developments of Methods and Critique of ad hoc Methods James R. Carpenter and Michael G. Kenward

Likelihood and Bayesian Methods
Introduction and Overview Michael G. Kenward, Geert Molenberghs, and Geert Verbeke

Perspective and Historical Overview Michael G. Kenward and Geert Molenberghs

Bayesian Methods Michael J. Daniels and Joseph W. Hogan

Joint Modeling of Longitudinal and Time-to-Event Data Dimitris Rizopoulos

Semi-Parametric Methods
Introduction and Overview Garrett M. Fitzmaurice

Missing Data Methods: A Semi-Parametric Perspective Anastasios A. Tsiatis and Marie Davidian

Double-Robust Methods Andrea Rotnitzky and Stijn Vansteelandt

Pseudo-Likelihood Methods for Incomplete Data Geert Molenberghs and Michael G. Kenward

Multiple Imputation
Introduction Michael G. Kenward

Multiple Imputation: Perspective and Historical Overview John B. Carlin

Fully Conditional Specification Stef van Buuren

Multilevel Multiple Imputation Harvey Goldstein and James R. Carpenter

Sensitivity Analysis
Introduction and Overview Geert Molenberghs, Geert Verbeke, and Michael G. Kenward

A Likelihood-Based Perspective Geert Verbeke, Geert Molenberghs, and Michael G. Kenward

A Semi-Parametric Perspective Stijn Vansteelandt

Bayesian Sensitivity Analysis Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu

Sensitivity Analysis with Multiple Imputation James R. Carpenter and Michael G. Kenward

The Elicitation and Use of Expert Opinion Ian R. White

Special Topics
Introduction and Overview Geert Molenberghs

Missing Data in Clinical Trials Craig Mallinckrodt

Missing Data in Sample Surveys Thomas R. Belin and Juwon Song

Model Diagnostics Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke

Index