Sampayo-Cordero Miguel, Miguel-Huguet Bernat, Malfettone Andrea, López-Miranda Elena, Gion María, Abad Elena, Alcalá-López Daniel, Pérez-Escuredo Jhudit, Pérez-García José Manuel, Llombart-Cussac Antonio, Cortés Javier
Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain.
Gerència Territorial Metropolitana Sud, Institut Català De La Salud, Hospital Universitari De Bellvitge, Barcelona, Spain.
Front Oncol. 2023 Jul 11;13:1048242. doi: 10.3389/fonc.2023.1048242. eCollection 2023.
De-escalation trials in oncology evaluate therapies that aim to improve the quality of life of patients with low-risk cancer by avoiding overtreatment. Non-inferiority randomized trials are commonly used to investigate de-intensified regimens with similar efficacy to that of standard regimens but with fewer adverse effects (ESMO evidence tier A). In cases where it is not feasible to recruit the number of patients needed for a randomized trial, single-arm prospective studies with a hypothesis of non-inferiority can be conducted as an alternative. Single-arm studies are also commonly used to evaluate novel treatment strategies (ESMO evidence tier B). A single-arm design that includes both non-inferiority and superiority primary objectives will enable the ranking of clinical activity and other parameters such as safety, pharmacokinetics, and pharmacodynamics data. Here, we describe the statistical principles and procedures to support such a strategy. The non-inferiority margin is calculated using the fixed margin method. Sample size and statistical analyses are based on the maximum likelihood method for exponential distributions. We present example analyses in metastatic and adjuvant settings to illustrate the usefulness of our methodology. We also explain its implementation with nonparametric methods. Single-arm designs with non-inferiority and superiority analyses are optimal for proof-of-concept and de-escalation studies in oncology.
肿瘤学中的降阶梯试验评估旨在通过避免过度治疗来改善低风险癌症患者生活质量的疗法。非劣效性随机试验通常用于研究疗效与标准方案相似但不良反应较少的降强度方案(ESMO证据等级A)。在招募随机试验所需患者数量不可行的情况下,可以进行以非劣效性为假设的单臂前瞻性研究作为替代方案。单臂研究也常用于评估新型治疗策略(ESMO证据等级B)。包含非劣效性和优效性主要目标的单臂设计将能够对临床活性以及安全性、药代动力学和药效学数据等其他参数进行排名。在此,我们描述支持这种策略的统计原理和程序。非劣效性界值采用固定界值法计算。样本量和统计分析基于指数分布的最大似然法。我们给出转移性和辅助治疗环境中的示例分析,以说明我们方法的实用性。我们还解释了其在非参数方法中的实施。具有非劣效性和优效性分析的单臂设计对于肿瘤学中的概念验证和降阶梯研究是最优的。