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![]( "Buchcover ISBN 9781118762509")
„In summary, the book is a must-have tool for any biostatistician dealing with missing data. It is an excellent reference book for postgraduate students or researchers working in the area of missing data.“ (Biometrical Journal, 1 June 2015)
„This is an excellent addition to the field, dealing with problems arising from missing data or unobserved data in clinical trials. It successfully bridges the gap between clinicians and statisticians using relatively common language to build common ground.“ (Doody's, 9 January 2015)
This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in thebiopharmaceutical industry, medical and public healthorganisations. Academics and students needing an introduction tohandling missing data will also find this book invaluable.
The authors describe how missing data can affect the outcome andcredibility of a clinical trial, show by examples how a clinicalteam can work to prevent missing data, and present the reader withapproaches to address missing data effectively.
The book is illustrated throughout with realistic case studies andworked examples, and presents clear and concise guidelines toenable good planning for missing data. The authors show how tohandle missing data in a way that is transparent and easy tounderstand for clinicians, regulators and patients. Newdevelopments are presented to improve the choice and implementationof primary and sensitivity analyses for missing data. Many SAS codeexamples are included - the reader is given a toolbox forimplementing analyses under a variety of assumptions.
The authors describe how missing data can affect the outcome andcredibility of a clinical trial, show by examples how a clinicalteam can work to prevent missing data, and present the reader withapproaches to address missing data effectively.
The book is illustrated throughout with realistic case studies andworked examples, and presents clear and concise guidelines toenable good planning for missing data. The authors show how tohandle missing data in a way that is transparent and easy tounderstand for clinicians, regulators and patients. Newdevelopments are presented to improve the choice and implementationof primary and sensitivity analyses for missing data. Many SAS codeexamples are included - the reader is given a toolbox forimplementing analyses under a variety of assumptions.
