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神经外科肿瘤学中亚组分析的实践与统计学方面:来自先锋联盟的全面综述

Practical and statistical aspects of subgroup analyses in surgical neuro-oncology: A comprehensive review from the PIONEER consortium.

作者信息

Gerritsen Jasper K W, Karschnia Philipp, Young Jacob S, van den Bent Martin J, Chang Susan M, Smith Timothy R, Nahed Brian V, Rincon-Torroella Jordina, Bettegowda Chetan, Sanai Nader, Krieg Sandro M, Maruyama Takashi, Schucht Philippe, Broekman Marike L D, Tonn Joerg-Christian, Wen Patrick Y, De Vleeschouwer Steven, Vincent Arnaud J P E, Hervey-Jumper Shawn, Berger Mitchel S, Mekary Rania A, Molinaro Annette M

机构信息

Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands.

Department of Neurosurgery, University of California, San Francisco, California, USA.

出版信息

Neuro Oncol. 2025 Jun 21;27(5):1149-1164. doi: 10.1093/neuonc/noae261.

Abstract

Subgroup analyses are essential to generate new hypotheses or to estimate treatment effects in clinically meaningful subgroups of patients. They play an important role in taking the next step toward personalized surgical treatment for brain tumor patients. However, subgroup analyses must be used with consideration and care because they have significant potential risks. Although some recommendations are available on the pearls and pitfalls of these analyses, a comprehensive guide is lacking, especially one focused on surgical neuro-oncology patients. This paper, therefore, reviews and summarizes for the first time comprehensively the practical and statistical considerations that are critical to this field. First, we evaluate the considerations when choosing a study design for surgical neuro-oncology studies and examine those unique to this field. Second, we give an overview of the relevant aspects to interpret subgroup analyses adequately. Third, we discuss the practical and statistical elements necessary to appropriately design and use subgroup analyses. The paper aims to provide an in-depth and complete guide to better understand risk modeling and assist the reader with practical examples of designing, using, and interpreting subgroup analyses.

摘要

亚组分析对于生成新假设或估计临床意义上的患者亚组中的治疗效果至关重要。它们在迈向脑肿瘤患者个性化手术治疗的下一步中发挥着重要作用。然而,亚组分析必须谨慎使用,因为它们存在重大潜在风险。尽管对于这些分析的要点和陷阱有一些建议,但缺乏全面的指南,尤其是针对手术神经肿瘤学患者的指南。因此,本文首次全面回顾并总结了该领域至关重要的实践和统计学考量。首先,我们评估为手术神经肿瘤学研究选择研究设计时的考量,并审视该领域特有的考量。其次,我们概述充分解释亚组分析的相关方面。第三,我们讨论适当设计和使用亚组分析所需的实践和统计学要素。本文旨在提供一份深入且完整的指南,以更好地理解风险建模,并通过设计、使用和解释亚组分析的实际示例帮助读者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfd/12187369/c75abf98e6c1/noae261_fig1.jpg

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