在利用外部数据增强传统临床研究中,使用倾向得分加权来增强权力的操作特征。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Gregory Alexander, Yunling Xu, Lilly Q Yue
{"title":"在利用外部数据增强传统临床研究中,使用倾向得分加权来增强权力的操作特征。","authors":"Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Gregory Alexander, Yunling Xu, Lilly Q Yue","doi":"10.1002/pst.2471","DOIUrl":null,"url":null,"abstract":"<p><p>The method of power prior has long been used as a tool for leveraging external data to augment a traditional clinical study. More recently, it has been found that integrating propensity scoring into its application has the potential for improved operating characteristics. In this paper, we introduce a new propensity score-integrated power prior strategy which uses propensity score weighting and is distinctive from other such proposals in the literature. This strategy replaces the sufficient statistic in the original expression of power prior with a propensity score weighted version of it. A simulation study shows that the operating characteristics of the proposed weighting strategy compare favorably to those of the original power prior method when there is covariate imbalance, like the stratification strategy we first introduced.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e2471"},"PeriodicalIF":1.3000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Propensity Score Weighting to Enhance the Operating Characteristics of Power Prior in Leveraging External Data to Augment a Traditional Clinical Study.\",\"authors\":\"Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Gregory Alexander, Yunling Xu, Lilly Q Yue\",\"doi\":\"10.1002/pst.2471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The method of power prior has long been used as a tool for leveraging external data to augment a traditional clinical study. More recently, it has been found that integrating propensity scoring into its application has the potential for improved operating characteristics. In this paper, we introduce a new propensity score-integrated power prior strategy which uses propensity score weighting and is distinctive from other such proposals in the literature. This strategy replaces the sufficient statistic in the original expression of power prior with a propensity score weighted version of it. A simulation study shows that the operating characteristics of the proposed weighting strategy compare favorably to those of the original power prior method when there is covariate imbalance, like the stratification strategy we first introduced.</p>\",\"PeriodicalId\":19934,\"journal\":{\"name\":\"Pharmaceutical Statistics\",\"volume\":\"24 2\",\"pages\":\"e2471\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmaceutical Statistics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pst.2471\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2471","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

功率先验方法长期以来一直被用作利用外部数据来增强传统临床研究的工具。最近,人们发现将倾向评分整合到其应用中有可能改善操作特性。在本文中,我们引入了一种新的倾向得分整合的权力优先策略,该策略使用倾向得分加权,与其他文献中的建议有所不同。该策略用倾向得分加权的方式取代了权力先验原始表达中的充分统计量。仿真研究表明,当存在协变量不平衡时,如我们首先介绍的分层策略,所提出的加权策略的操作特性优于原始功率先验方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Propensity Score Weighting to Enhance the Operating Characteristics of Power Prior in Leveraging External Data to Augment a Traditional Clinical Study.

The method of power prior has long been used as a tool for leveraging external data to augment a traditional clinical study. More recently, it has been found that integrating propensity scoring into its application has the potential for improved operating characteristics. In this paper, we introduce a new propensity score-integrated power prior strategy which uses propensity score weighting and is distinctive from other such proposals in the literature. This strategy replaces the sufficient statistic in the original expression of power prior with a propensity score weighted version of it. A simulation study shows that the operating characteristics of the proposed weighting strategy compare favorably to those of the original power prior method when there is covariate imbalance, like the stratification strategy we first introduced.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信