Mazza Gina L, Culakova Eva, Enserro Danielle M, Dignam James J, Unger Joseph M
Alliance Statistics and Data Management Center, Mayo Clinic, Scottsdale, AZ, United States.
Division of Supportive Care in Cancer, Department of Surgery, University of Rochester Medical Center, Rochester, NY, United States.
J Natl Cancer Inst Monogr. 2025 Mar 1;2025(68):22-29. doi: 10.1093/jncimonographs/lgae045.
Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute's National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.
鉴于对个性化医疗的追求以及治疗决策中代表性和公平性的重要性，研究由人口统计学、遗传学或临床特征定义的患者亚组中的治疗效果越来越受到关注。治疗效果的大小甚至方向可能在不同亚组之间有所不同，而这些差异治疗效果可能具有临床意义。然而，由于得出假阳性或假阴性结论的可能性很高，亚组分析在解释时需要谨慎。我们概述了在肿瘤学试验中负责任地研究和报告不同亚组间差异治疗效果的研究设计和分析注意事项，并列举了美国国立癌症研究所的国家临床试验网络和社区肿瘤学研究项目的例子。建议包括确保感兴趣亚组的患者有适当的代表性，认识到效能和多重性的局限性，并将探索性亚组分析视为产生假设而非改变实践。
