Clinical Trials With Missing Data A Guide for Practitioners

A practical guide for handling and planning for missing data in clinical trials, Clinical Trials with Missing Data provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents statisticians, biostatisticians, and researchers with approaches to effectively address them.

Clinical Trials with Missing Data A Guide for Practitioners

How to plan and execute a successful approach to missing data in most clinical trials.

Missing data occur in nearly all clinical trials. In order to maintain the credibility of results, it is vital that measures to minimize the amount of missing data are carried out and that appropriate analysis techniques are identified and implemented correctly. 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.

This book:

• Presents clear and concise guidelines to enable good planning for missing data
• Is illustrated throughout with realistic case studies and worked examples
• Demonstrates how missing data can be treated in a way that is transparent and easy to understand for clinicians, regulators, and patients
• Explores new developments in the choice and implementation of primary and sensitivity analyses for missing data
• Includes many SAS code examples – a toolbox for implementing analyses under a variety of assumptions

Clinical Trials with Missing Data provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organizations. Academics and students needing an introduction to handling missing data will also find this book invaluable.

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.