Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science—multiple imputation—fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise.

Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author’s package MICE is included throughout the book.

Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences:

(Bio)statisticians, epidemiologists, and methodologists in the social and health sciences Substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes

This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.

Reviews

I’m delighted to see this new book on multiple imputation by Stef van Buuren …This book represents a "no nonsense" straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays … It’s great to have Stef’s book on multiple imputation, and I look forward to seeieditions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice.

—From the Foreword by Donald B. Rubin

Contents

Basics

Introduction

The problem of missing data

Concepts of MCAR, MAR and MNAR

Simple solutions that do not (always) work

Multiple imputation in a nutshell

Goal of the book

What the book does not cover

Structure of the book

Exercises

Multiple imputation

Historic overview

Incomplete data concepts

Why and when multiple imputation works

Statistical intervals and tests

Evaluation criteria

When to use multiple imputation

How many imputations?

Exercises

Univariate missing data

How to generate multiple imputations

Imputation under the normal linear normal

Imputation under non-normal distributions

Predictive mean matching

Categorical data

Other data types

Cl...