找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
樓主: 富裕
31#
發(fā)表于 2025-3-27 00:35:55 | 只看該作者
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
32#
發(fā)表于 2025-3-27 03:27:03 | 只看該作者
33#
發(fā)表于 2025-3-27 06:47:15 | 只看該作者
34#
發(fā)表于 2025-3-27 12:32:20 | 只看該作者
35#
發(fā)表于 2025-3-27 14:01:27 | 只看該作者
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.
36#
發(fā)表于 2025-3-27 20:34:00 | 只看該作者
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.
37#
發(fā)表于 2025-3-28 00:21:45 | 只看該作者
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.
38#
發(fā)表于 2025-3-28 04:30:06 | 只看該作者
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.
39#
發(fā)表于 2025-3-28 10:01:08 | 只看該作者
40#
發(fā)表于 2025-3-28 12:03:11 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-25 15:38
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
石景山区| 濮阳县| 营山县| 甘谷县| 九台市| 平罗县| 周至县| 武穴市| 浦江县| 磴口县| 历史| 孝义市| 雅安市| 永吉县| 平江县| 浦城县| 秦安县| 哈巴河县| 咸阳市| 黑河市| 仁化县| 琼结县| 西青区| 平定县| 同江市| 德钦县| 通道| 芮城县| 奈曼旗| 芜湖县| 衡阳县| 剑阁县| 威宁| 沽源县| 台湾省| 张北县| 大荔县| 咸阳市| 双牌县| 镇远县| 永善县|