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RRApp,一款功能强大的随机化应用程序,用于临床和转化研究。

RRApp, a robust randomization app, for clinical and translational research.

作者信息

Tu Chengcheng, Benn Emma K T

机构信息

Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

New York College of Podiatric Medicine, New York, NY, USA.

出版信息

J Clin Transl Sci. 2017 Dec;1(6):323-327. doi: 10.1017/cts.2017.310. Epub 2018 Feb 19.

DOI:10.1017/cts.2017.310
PMID:29707253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5915802/
Abstract

While junior clinical researchers at academic medical institutions across the US often desire to be actively engaged in randomized-clinical trials, they often lack adequate resources and research capacity to design and implement them. This insufficiency hinders their ability to generate a rigorous randomization scheme to minimize selection bias and yield comparable groups. Moreover, there are limited online user-friendly randomization tools. Thus, we developed a free robust randomization app (RRApp). RRApp incorporates 6 major randomization techniques: simple randomization, stratified randomization, block randomization, permuted block randomization, stratified block randomization, and stratified permuted block randomization. The design phase has been completed, including robust server scripts and a straightforward user-interface using the "shiny" package in R. Randomization schemes generated in RRApp can be input directly into the Research Electronic Data Capture (REDCap) system. RRApp has been evaluated by biostatisticians and junior clinical faculty at the Icahn School of Medicine at Mount Sinai. Constructive feedback regarding the quality and functionality of RRApp was also provided by attendees of the 2016 Association for Clinical and Translational Statisticians Annual Meeting. RRApp aims to educate early stage clinical trialists about the importance of randomization, while simultaneously assisting them, in a user-friendly fashion, to generate reproducible randomization schemes.

摘要

在美国各地的学术医疗机构中,初级临床研究人员虽然常常希望积极参与随机临床试验,但他们往往缺乏设计和实施此类试验的足够资源和研究能力。这种不足阻碍了他们制定严格随机方案以尽量减少选择偏倚并产生可比组的能力。此外,在线用户友好型随机工具也很有限。因此,我们开发了一款免费的强大随机化应用程序(RRApp)。RRApp纳入了6种主要随机技术:简单随机化、分层随机化、区组随机化、排列区组随机化、分层区组随机化和分层排列区组随机化。设计阶段已经完成,包括强大的服务器脚本以及使用R语言中的“shiny”包的直观用户界面。RRApp生成的随机方案可直接输入到研究电子数据采集(REDCap)系统中。RRApp已由西奈山伊坎医学院的生物统计学家和初级临床教员进行了评估。2016年临床与转化统计学家协会年会的与会者也提供了关于RRApp质量和功能的建设性反馈。RRApp旨在让早期临床试验人员了解随机化的重要性,同时以用户友好的方式协助他们生成可重复的随机方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f974/6798192/e192c43f8c8b/S2059866117003107_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f974/6798192/f4152fa4add0/S2059866117003107_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f974/6798192/e192c43f8c8b/S2059866117003107_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f974/6798192/f4152fa4add0/S2059866117003107_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f974/6798192/e192c43f8c8b/S2059866117003107_fig2.jpg

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