子群分析
医学
医学物理学
统计分析
领域(数学)
梅德林
肿瘤科
计算机科学
内科学
统计
荟萃分析
数学
纯数学
政治学
法学
作者
Jasper Kees Wim Gerritsen,Philipp Karschnia,Jacob S. Young,Martin J. van den Bent,Susan Chang,Timothy R. Smith,Brian V. Nahed,Jordina Rincón-Torroella,Chetan Bettegowda,Nader Sanai,Sandro M. Krieg,Takashi Maruyama,Philippe Schucht,Marike L D Broekman,Joerg-Christian Tonn,Patrick Y. Wen,Steven De Vleeschouwer,Arnaud J.P.E. Vincent,Shawn L. Hervey‐Jumper,Mitchel S. Berger,Rania A. Mekary,Annette M. Molinaro
出处
期刊:Neuro-oncology
[Oxford University Press]
日期:2024-12-07
标识
DOI:10.1093/neuonc/noae261
摘要
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 towards 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.
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