A Bayesian two-stage group sequential scheme for ordinal endpoints

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-01 DOI:10.1093/jrsssc/qlad026
Chengxue Zhong, Hongyu Miao, H. Pan
{"title":"A Bayesian two-stage group sequential scheme for ordinal endpoints","authors":"Chengxue Zhong, Hongyu Miao, H. Pan","doi":"10.1093/jrsssc/qlad026","DOIUrl":null,"url":null,"abstract":"\n Ordinal endpoints are common in clinical studies. For example, many clinical trials for evaluating COVID-19 infection therapies have adopted an ordinal scale as recommended by the World Health Organization. Despite their importance in clinical studies, design methods for ordinal endpoints are limited; in practice, a dichotomized approach is often used for simplicity. Here, we introduce a Bayesian group sequential scheme to assess ordinal endpoints, which considers a proportional-odds (PO) model, a nonproportional-odds (NPO) model, and a PO/NPO-switch model to handle various scenarios. Extensive simulations are conducted to demonstrate desirable performance, and the R package BayesOrdDesign has been made publicly available.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Royal Statistical Society Series C-Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/jrsssc/qlad026","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0

Abstract

Ordinal endpoints are common in clinical studies. For example, many clinical trials for evaluating COVID-19 infection therapies have adopted an ordinal scale as recommended by the World Health Organization. Despite their importance in clinical studies, design methods for ordinal endpoints are limited; in practice, a dichotomized approach is often used for simplicity. Here, we introduce a Bayesian group sequential scheme to assess ordinal endpoints, which considers a proportional-odds (PO) model, a nonproportional-odds (NPO) model, and a PO/NPO-switch model to handle various scenarios. Extensive simulations are conducted to demonstrate desirable performance, and the R package BayesOrdDesign has been made publicly available.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
有序端点的贝叶斯两阶段群序列格式
顺序终点在临床研究中很常见。例如,许多评估COVID-19感染治疗的临床试验采用了世界卫生组织推荐的顺序量表。尽管它们在临床研究中很重要,但顺序终点的设计方法是有限的;在实践中,为了简单起见,通常使用二分法。在这里,我们引入了一个贝叶斯群序列方案来评估有序端点,该方案考虑了比例几率(PO)模型、非比例几率(NPO)模型和PO/NPO切换模型来处理各种场景。进行了大量的模拟以证明理想的性能,并且R包BayesOrdDesign已经公开可用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
期刊最新文献
tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction. Measuring the impact of new risk factors within survival models. Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants. Joint modelling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment in the U.S. Walking fingerprinting.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1