𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Sample Size Determination in Clinical Trials with Multiple Endpoints

✍ Scribed by Takashi Sozu, Tomoyuki Sugimoto, Toshimitsu Hamasaki, Scott R. Evans (auth.)


Publisher
Springer International Publishing
Year
2015
Tongue
English
Leaves
98
Series
SpringerBriefs in Statistics
Edition
1
Category
Library

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✦ Synopsis


This book integrates recent methodological developments for calculating the sample size and power in trials with more than one endpoint considered as multiple primary or co-primary, offering an important reference work for statisticians working in this area.

The determination of sample size and the evaluation of power are fundamental and critical elements in the design of clinical trials. If the sample size is too small, important effects may go unnoticed; if the sample size is too large, it represents a waste of resources and unethically puts more participants at risk than necessary. Recently many clinical trials have been designed with more than one endpoint considered as multiple primary or co-primary, creating a need for new approaches to the design and analysis of these clinical trials. The book focuses on the evaluation of power and sample size determination when comparing the effects of two interventions in superiority clinical trials with multiple endpoints. Methods for sample size calculation in clinical trials where the alternative hypothesis is that there are effects on ALL endpoints are discussed in detail. The book also briefly examines trials designed with an alternative hypothesis of an effect on AT LEAST ONE endpoint with a prespecified non-ordering of endpoints.

✦ Table of Contents


Front Matter....Pages i-vi
Introduction....Pages 1-6
Continuous Co-primary Endpoints....Pages 7-23
Binary Co-primary Endpoints....Pages 25-39
Convenient Sample Size Formula....Pages 41-58
Continuous Primary Endpoints....Pages 59-68
Further Developments....Pages 69-74
Back Matter....Pages 75-95

✦ Subjects


Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics


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