Target RWE通过新出版物推动了真实世界证据中下一代因果推断方法的发展-动脉网

Target RWE Advances Next-Generation Causal Inference Approaches in Real-World Evidence with New Publications

CISION 等信源发布 2025-06-23 21:00

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DURHAM, N.C.

杜伦，北卡罗来纳州

，

June 23, 2025

2025年6月23日

/PRNewswire/ -- Target RWE, a leader in clinical evidence generation, has reinforced its position at the forefront of generating real-world evidence (RWE) with the publication of two key epidemiological papers. The publications introduce structured, principled approaches that enhance the validity and applicability of RWE, pushing beyond traditional propensity score methods to deliver more robust and generalizable insights..

/PRNewswire/ -- 作为临床证据生成领域的领导者，Target RWE通过发表两篇关键的流行病学论文，进一步巩固了其在生成真实世界证据 (RWE) 方面的前沿地位。这些论文介绍了结构化且有原则的方法，提升了RWE的有效性和适用性，超越了传统的倾向评分方法，提供了更强大且更具普适性的洞察。

Published in

发布于

Clinical Pharmacology & Therapeutics

临床药理学与治疗学

and titled

并且标题为

Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence

推进基于原则的药物流行病学研究以支持监管和医疗决策：真实世界证据的时代

, the first publication details four key areas that are driving the evolution of RWE. This includes the development of increasingly large and rich healthcare datasets that capture comprehensive patient journeys; advanced technologies including artificial intelligence that extract valuable insights from unstructured clinical data; advancements in principled study design and analytical approaches that strengthen causal inference; and robust frameworks that support transparent study conduct and reproducibility..

，第一篇出版物详细介绍了推动真实世界证据（RWE）发展的四个关键领域。其中包括捕捉全面患者旅程的日益庞大且丰富的医疗数据集；包括人工智能在内的先进技术，从非结构化临床数据中提取有价值的见解；原则性研究设计和分析方法的进步，增强了因果推断的可靠性；以及支持研究透明度和可重复性的强大框架。

'Target RWE routinely implements the advanced principled research methodologies that are presented in these publications, which demonstrates our leadership in shaping the future of real word evidence generation. We are not only setting new standards for clinical evidence generation, but also empowering regulators, clinicians, and decision-makers to address critical healthcare challenges with confidence.

“目标RWE routinely实施这些出版物中介绍的先进原则性研究方法，这证明了我们在塑造真实世界证据生成未来方面的领导地位。我们不仅在为临床证据生成设定新标准，还赋予监管者、临床医生和决策者以信心应对关键的医疗挑战。

These studies reflect the transformative impact of RWE in filling evidence gaps, driving innovation, and paving the way for advanced pharmacoepidemiologic research that improves patient outcomes,' said Target RWE Chief Scientific Officer .

这些研究反映了真实世界证据 (RWE) 在填补证据空白、推动创新以及为改善患者预后的先进药物流行病学研究铺平道路方面的变革性影响，Target RWE 首席科学官表示。

Jennifer Christian

詹妮弗·克里斯蒂安

, PharmD, PhD, FISPE.

，药学博士，哲学博士，FISPE。

The second publication, titled

第二个出版物，标题为

Propensity Score Methods for Confounding Control in Observational Studies of Therapeutics for COVID-19 Infection

用于控制COVID-19感染治疗观察性研究中混杂因素的倾向评分方法

, addresses the limitations in observational research and propensity score methodologies which historically restrict inference to subpopulations receiving specific treatments - using the COVID-19 pandemic as its setting.

，解决了观察性研究和倾向评分方法学中的局限性，这些局限性历来将推论限制在接受特定治疗的亚人群中——以 COVID-19 大流行作为其背景。

'While propensity score methods represent a significant methodological advancement, it is often not well understood that the interpretation of the results depends on how the methods are used,' explained M.

“虽然倾向评分方法代表了重要的方法学进步，但人们往往并不充分理解，结果的解释取决于这些方法的使用方式，”M解释道。

Alan Brookhart

艾伦·布鲁克哈特

, PhD, Target RWE Scientific Advisor and co-author of the study. 'These issues are particularly important in studies of treatments for COVID-19.'

博士，Target RWE科学顾问兼该研究的合著者。 “这些问题在针对COVID-19治疗的研究中尤为重要。”

Advanced pharmacoepidemiology methods, including inverse probability of treatment weighted analyses allow researchers to draw inferences about treatment effects across the entire targeted population of interest. Rather than standardizing populations to resemble treatment recipients in matched propensity score analyses, inverting and weighting propensity scores across the entire eligible population enables broader inference..

先进的药物流行病学方法，包括逆概率加权分析，使研究人员能够推断整个目标人群的治疗效果。与在匹配倾向评分分析中将人群标准化以类似于治疗接受者不同，对整个符合条件的人群进行倾向评分的倒置和加权可以实现更广泛的推断。

Target RWE is implementing these methodological advancements

目标RWE正在实施这些方法论的改进。

across its research platforms by generating real-world data to build comprehensive patient journeys; employing LLMs and AI with manual curation for validation to extract insights from unstructured clinical data; advancing beyond traditional propensity score methods to embrace sophisticated causal inference techniques; and establishing clean room committee approaches for enhanced methodological oversight throughout the conduct of the study..

通过生成真实世界的数据，在其研究平台上构建全面的患者旅程；运用大型语言模型（LLM）和人工智能结合人工审核进行验证，从非结构化临床数据中提取洞察；超越传统的倾向评分方法，采用复杂的因果推断技术；并建立清洁室委员会方法，以在整个研究过程中加强方法学监督。

Further, Target RWE's proprietary software platform,

此外，Target RWE的专有软件平台，

causalStudio™

因果工作室™

, addresses real-world challenges by providing a software solution that leverages validated analytics proven and trusted for regulatory decision-making. causalStudio™ is comprised of two unique components: causalRisk™ - a software package designed to simplify the complex process of conducting causal inference studies, and causalPHR™ - an intuitive and interactive tool to visualize, stratify, filter, and publish analytical results.

，通过提供利用经过验证的分析方法的软件解决方案来应对现实世界的挑战，这些分析方法已被证明并受信任可用于监管决策。causalStudio™ 由两个独特的组件组成：causalRisk™ - 一款旨在简化进行因果推断研究复杂过程的软件包，以及 causalPHR™ - 一个直观且交互式的工具，用于可视化、分层、筛选和发布分析结果。

Both causalRisk™ and causalPHR™ are data agnostic and developed by statisticians and statistical software developers at Target RWE..

causalRisk™ 和 causalPHR™ 均为数据不可知的工具，由 Target RWE 的统计学家和统计软件开发人员开发。

Through the integration of advanced causal inference methodologies and principled epidemiologic approaches, Target RWE ensures that its data and analytics provide a comprehensive and accurate representation of patient experiences in real-world clinical settings.

通过整合先进的因果推断方法和有原则的流行病学方法，Target RWE 确保其数据和分析能够全面且准确地反映患者在真实临床环境中的体验。

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访问

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我们的网站

or contact

或联系

info@targetrwe.com

to learn more about our advanced analytical solutions today!

立即了解更多关于我们先进的分析解决方案！

SOURCE Target RWE

源目标RWE