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Titlebook: Sample Size Determination in Clinical Trials with Multiple Endpoints; Takashi Sozu,Tomoyuki Sugimoto,Scott R. Evans Book 2015 The Author(s

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發(fā)表于 2025-3-21 19:40:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Sample Size Determination in Clinical Trials with Multiple Endpoints
編輯Takashi Sozu,Tomoyuki Sugimoto,Scott R. Evans
視頻videohttp://file.papertrans.cn/861/860732/860732.mp4
概述Reviews statistical issues in clinical trials with multiple endpoints.Describes methods for power and sample size calculations in clinical trials with multiple endpoints including recently developed a
叢書名稱SpringerBriefs in Statistics
圖書封面Titlebook: Sample Size Determination in Clinical Trials with Multiple Endpoints;  Takashi Sozu,Tomoyuki Sugimoto,Scott R. Evans Book 2015 The Author(s
描述.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 brie?y examines trials designed with an alternat
出版日期Book 2015
關(guān)鍵詞Binary endpoints; Clinical tirals; Multiple endpoints; Power calculation; Sample size
版次1
doihttps://doi.org/10.1007/978-3-319-22005-5
isbn_softcover978-3-319-22004-8
isbn_ebook978-3-319-22005-5Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s) 2015
The information of publication is updating

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Sample Size Determination in Clinical Trials with Multiple Endpoints
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ng images had, such as low image resolution, high similarity, and a large volume of data, the deep learning-based approach shows superior performance to detect weeds in heterogeneous landscapes. Our findings will enhance remote sensing capabilities in the Australian weed community through knowledge
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Takashi Sozu,Tomoyuki Sugimoto,Toshimitsu Hamasaki,Scott R. Evans classifiers trained on source classifier to predict target samples. Thus we deploy a robust deep logistic regression loss on the target samples, resulting in our RDLR model. In such a way, pseudo-labels are gradually assigned to unlabeled target samples according to their maximum classification sco
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from cameras system. Results were totally satisfactory with 100% effectiveness in a range of 5% to 95% with respect to the H component of the HSV scheme. The proposed method recognizes and locates utility poles with respect to the stereo vision system.
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