在真实世界环境中,在没有个体患者数据的情况下,将治疗效果推广到目标人群。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-09-03 DOI:10.1002/pst.2435
Hui Quan, Tong Li, Xun Chen, Gang Li
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引用次数: 0

摘要

创新性地使用真实世界数据(RWD)可以回答随机临床试验(RCT)数据无法回答的问题。随机临床试验的赞助商拥有一个中央数据库,其中包含从试验中收集的所有患者个人数据(IPD),而真实世界数据的分析人员却面临着一个挑战:由于患者隐私方面的规定,从所有地区获取 IPD 在逻辑上是不可能的。在这项研究中,我们为分析发起人提出了一种双反概率加权(DIPW)方法,以便在无需获取 IPD 的情况下估算目标人群的人群平均治疗效果(PATE)。一种概率加权是为了实现各治疗组混杂因素分布的可比性;另一种概率加权是为了将结果从拥有终点数据的亚群患者推广到整个目标人群。倾向评分的似然表达式和 PATE 的 DIPW 估计器可以写成只依赖于不需要 IPD 的区域汇总统计。我们的方法取决于正相关性和条件独立性假设,这是大多数 RWD 分析方法的先决条件。我们进行了模拟,以比较所提议的方法与修正元分析和常规元分析的性能。
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Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

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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.
期刊最新文献
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