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
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
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
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
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
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
Geert Molenberghs, Garrett Fitzmaurice, Michael G. Kenward, Anastasios Tsiatis, Geert Verbeke
"There is evidence of a strong editorial hand—each chapter begins with a table of contents; the notation is surprisingly well standardized for a work by 20 authors; and the number of typos is modest. The chapters refer to each other, but one can read them independently and in any order...This handbook summarizes the authors’ research on a range of missing-data problems of contemporary interest. Methodologists who seek a one-volume entry point into the field will find it useful."
—Journal of the American Statistical Association, May 2016