Brief Guidelines for Methods and Statistics in Medical Research, Jamalludin Bin Ab Rahman

Автор: Richard A. Johnson, Gouri K. Bhattacharyya
Название: Statistics: Principles and Methods, 7th Edition
ISBN: 0470904119 ISBN-13(EAN): 9780470904114
Издательство: Wiley
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Цена: 35369.00 р.
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Описание: Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include real-world data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods.

Автор: J. Philip Miller
Название: Essential Statistical Methods for Medical Statistics,
ISBN: 0444537376 ISBN-13(EAN): 9780444537379
Издательство: Elsevier Science
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Цена: 8541.00 р.
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Описание: Addresses statistical challenges in epidemiological, biomedical, and pharmaceutical research. This book presents methods for assessing Biomarkers, analysis of competing risks. It offers clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs.

Автор: E.A. Gehan; N.A. Lemak
Название: Statistics in Medical Research
ISBN: 1461360722 ISBN-13(EAN): 9781461360728
Издательство: Springer
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Цена: 6986.00 р.
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Автор: Altman
Название: Practical Statistics for Medical Research
ISBN: 0412276305 ISBN-13(EAN): 9780412276309
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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Описание: Primarily aimed at medical researchers, this book aims to provide an understanding of the basic principles that underlie research design, data analysis and the interpretation of results, and to enable the reader to carry out a wide range of statistical analyses.

Название: Clinical Trial Optimization using R
ISBN: 149873507X ISBN-13(EAN): 9781498735070
Издательство: Taylor&Francis
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Цена: 16078.00 р.
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Описание:

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making.