倾向得分纳入自适应设计方法时,纳入现实世界的数据。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Pharmaceutical Statistics Pub Date : 2024-03-01 Epub Date: 2023-11-28 DOI:10.1002/pst.2347
Nelson Lu, Wei-Chen Chen, Heng Li, Changhong Song, Ram Tiwari, Chenguang Wang, Yunling Xu, Lilly Q Yue
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引用次数: 0

摘要

倾向得分整合复合似然(PSCL)方法是一种可用于设计和分析应用程序的方法,当利用真实世界数据(RWD)来增强前瞻性设计的临床研究时。在PSCL中,地层是基于倾向分数(PS)形成的,这样就可以将来自当前研究和RWD来源的基线协变量方面的相似对象放置在同一地层中,然后使用复合似然方法来降低RWD信息的权重。虽然PSCL最初是针对固定设计提出的,但它可以扩展到自适应设计框架下的应用,目的是潜在地声称早期成功或重新估计样本量。本文针对PSCL的特点,提出了一种通用策略。对于声称早期成功的可能性,使用Fisher的组合试验。当目的是重新估计样本量时,建议的程序是基于Cui, Hung和Wang提出的检验。通过实例说明了这两个过程的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Propensity score-incorporated adaptive design approaches when incorporating real-world data.

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

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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.
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