找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

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

[复制链接]
查看: 22604|回复: 39
发表于 2025-3-21 19:40:53 | 显示全部楼层 |阅读模式
书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints
编辑Takashi Sozu,Tomoyuki Sugimoto,Scott R. Evans
视频video
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
丛书名称SpringerBriefs in Statistics
图书封面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 briefly examines trials designed with an alternat
出版日期Book 2015
关键词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

书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints影响因子(影响力)




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints影响因子(影响力)学科排名




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints网络公开度




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints网络公开度学科排名




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints被引频次




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints被引频次学科排名




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints年度引用




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints年度引用学科排名




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints读者反馈




书目名称Sample Size Determination in Clinical Trials with Multiple Endpoints读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 23:33:47 | 显示全部楼层
Sample Size Determination in Clinical Trials with Multiple Endpoints
发表于 2025-3-22 01:22:51 | 显示全部楼层
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
发表于 2025-3-22 07:31:42 | 显示全部楼层
发表于 2025-3-22 10:36:55 | 显示全部楼层
发表于 2025-3-22 14:02:44 | 显示全部楼层
发表于 2025-3-22 18:49:30 | 显示全部楼层
发表于 2025-3-22 23:37:49 | 显示全部楼层
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
发表于 2025-3-23 04:57:11 | 显示全部楼层
发表于 2025-3-23 06:03:55 | 显示全部楼层
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.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-19 17:47
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表