Suppr超能文献

PROpwr:一个用于分析患者报告结局数据并估计检验效能的R语言闪亮应用程序。

PROpwr: a Shiny R application to analyze patient-reported outcomes data and estimate power.

作者信息

Hu Jinxiang, Mei Xiaohang, Pepper Sam, Wang Yu, Zhang Bo, Cernik Colin, Gajewski Byron

机构信息

Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.

Biometrics, Bristol Myers Squibb, USA.

出版信息

J Biopharm Stat. 2025 Aug;35(5):969-980. doi: 10.1080/10543406.2024.2365966. Epub 2024 Jun 13.

Abstract

Patient Reported Outcomes (PROs) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. We propose this R shiny web application, PROpwr, that estimates power for two-arm clinical trials with PRO measures as endpoints using Item Response Theory (GRM: Graded Response Model) and simulations. PROpwr also supports the analysis of PRO data for convenience of estimating the effect size. There are seven function tabs in PROpwr: Frequentist Analysis, Bayesian Analysis, GRM power, T-test Power Given Sample Size, T-test Sample Size Given Power, Download, and References. PROpwr is user-friendly with point-and-click functions. PROpwr can assist researchers to analyze and calculate power and sample size for PRO endpoints in clinical trials without prior programming knowledge.

摘要

患者报告结局(PROs)广泛应用于生活质量(QOL)研究、健康结局研究和临床试验。卫生当局倡导了PRO的重要性。我们提出了这个R闪亮网络应用程序PROpwr,它使用项目反应理论(GRM:等级反应模型)和模拟来估计以PRO测量为终点的双臂临床试验的效能。为了便于估计效应大小,PROpwr还支持对PRO数据的分析。PROpwr中有七个功能标签:频率论分析、贝叶斯分析、GRM效能、给定样本量时的t检验效能、给定效能时的t检验样本量、下载和参考文献。PROpwr具有点击式功能,用户友好。PROpwr可以帮助研究人员在无需事先编程知识的情况下,分析和计算临床试验中PRO终点的效能和样本量。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验