Multiple Imputation and Its Application - (Statistics in Practice) 2nd Edition (Hardcover)

About the Book

"Multiple imputation remains the most widely used methodology for missing data. Since the publication of the first edition, both MI methodology and the range of applications has continued to expand and develop. Methodological advances include extended MI methodologies for multilevel data and causal models, alongside important practical developments in sensitivity analysis. Key practical applications are clinical trials, prognostic modelling and causal modelling. Following on from the first edition, the authors here present the concepts in an intuitive way, setting out the issues raised by missing data, describing the rationale for MI, and show how it can be applied in increasingly complex settings with a range of examples. Also available for the first time are theoretical and computer-based exercises using Stata and R to help the instructor. Multiple Imputation and its Application, Second Edition is aimed at quantitative medical and social researchers by presenting the concepts in an intuitive way, illustrating with a range of examples. Alongside this, inclusion of key mathematical details, and theoretical and computer-based exercises will make the text suitable for graduate teaching and short courses"--

Book Synopsis

Multiple Imputation and its Application

The most up-to-date edition of a bestselling guide to analyzing partially observed data

In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.

With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find:

• A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets
• Careful discussion of key concepts
• A range of examples illustrating the key ideas
• Practical advice on using multiple imputation
• Exercises and examples designed for use in the classroom and/or private study

Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.

From the Back Cover

Multiple Imputation and its Application

Second Edition

The most up-to-date edition of a bestselling guide to analyzing partially observed data

In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.

With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find:

• A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets
• Careful discussion of key concepts
• A range of examples illustrating the key ideas
• Practical advice on using multiple imputation
• Exercises and examples designed for use in the classroom and/or private study

Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.

About the Author

JAMES R. CARPENTER is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine and Programme Leader in Methodology at the MRC Clinical Trials Unit at UCL, UK.

JONATHAN W. BARTLETT is a Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, UK.

TIM P. MORRIS is Principal Research Fellow in Medical Statistics at the MRC Clinical Trials Unit at UCL, UK.

ANGELA M. WOOD is Professor of Health Data Science in the Department of Public Health and Primary Care, University of Cambridge, UK.

MATTEO QUARTAGNO is Senior Research Fellow in Medical Statistics at the MRC Clinical Trials Unit at UCL, UK.

MICHAEL G. KENWARD retired in 2016 after sixteen years as GlaxoSmithKline Professor of Biostatistics at the London School of Hygiene & Tropical Medicine, UK.