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Clinical trials with missing data : a guide for practitioners / Michael O'Kelly and Bohdana Ratitch.

By: Contributor(s): Series: Statistics in practicePublication details: Chiester : John Wiley, c2014.Description: xxvii, 439 p. : ill. ; 24 cmISBN:
  • 9781118460702
Subject(s): DDC classification:
  • 000SB:610.724 23 Ok41
Contents:
1. What's the problem with missing data?-- 2. The prevention of missing data-- 3. Regulatory guidance-a quick tour-- 4. A guide to planning for missing data-- 5. Mixed models for repeated measures using categorical time effects (MMRM)-- 6. Multiple imputation-- 7. Analyses under missing-not-at-random assumptions-- 8. Doubly robust estimation-- Bibliography-- Index.
Summary: "This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included - the reader is given a toolbox for implementing analyses under a variety of assumptions"--Provided by publisher.
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Includes bibliographical references and index.

1. What's the problem with missing data?--
2. The prevention of missing data--
3. Regulatory guidance-a quick tour--
4. A guide to planning for missing data--
5. Mixed models for repeated measures using categorical time effects (MMRM)--
6. Multiple imputation--
7. Analyses under missing-not-at-random assumptions--
8. Doubly robust estimation--
Bibliography--
Index.

"This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included - the reader is given a toolbox for implementing analyses under a variety of assumptions"--Provided by publisher.

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