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: Efficacy Analysis in Clinical Trials an Update; Efficacy Analysis in Ton J. Cleophas,Aeilko H. Zwinderman Textbook 2019 Springer Nature Swi

[复制链接]
楼主: 富裕
发表于 2025-3-27 00:35:55 | 显示全部楼层
Alpha-Synuclein in Cerebrospinal Fluidlp of machine learning..Traditional efficacy analysis consisted of.simple linear regressions,.multiple linear regressions,.Bonferroni’s adjustments..Machine learning efficacy analysis consisted of gamma-distribution methods..The machine learning methods provided better sensitivity of testing, and we
发表于 2025-3-27 03:27:03 | 显示全部楼层
发表于 2025-3-27 06:47:15 | 显示全部楼层
发表于 2025-3-27 12:32:20 | 显示全部楼层
发表于 2025-3-27 14:01:27 | 显示全部楼层
A Shared (Cost) Burden (Pillar Three), and with the help of machine learning..Traditional efficacy analysis was consisted of.Machine learning efficacy analysis consisted of ratio-statistic methods..The machine learning methods provided better sensitivity of testing, and were more informative.
发表于 2025-3-27 20:34:00 | 显示全部楼层
https://doi.org/10.1007/978-3-540-78809-6achine learning..Traditional efficacy analysis consisted of.Machine learning efficacy analysis consisted of complex-samples methods..The machine learning methods provided better sensitivity of testing, and were more informative.
发表于 2025-3-28 00:21:45 | 显示全部楼层
https://doi.org/10.1007/978-1-4615-6805-6d with the help of machine learning..Traditional efficacy analysis was composed of.Poisson statistics,.z-tests..Machine learning efficacy analysis was composed of evolutionary-operation methods..The machine learning methods provided better sensitivity of testing, and were more informative.
发表于 2025-3-28 04:30:06 | 显示全部楼层
Daria Smirnova,Tatiana Smirnova,Paul Cummingnalysis was composed of.discretization of continuous predictors,.three dimensional bars of effects versus outcome,.crosstabs with chi-square statistics..Machine learning efficacy analysis was composed of high-risk-bin methods..The machine learning methods provided better sensitivity of testing, and were more informative.
发表于 2025-3-28 10:01:08 | 显示全部楼层
发表于 2025-3-28 12:03:11 | 显示全部楼层
 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, 2025-5-22 13:45
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