Dressler Emily V, Pugh Stephanie L, Gunn Heather J, Unger Joseph M, Zahrieh David M, Snavely Anna C
Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
NRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, PA 19103, United States.
J Natl Cancer Inst Monogr. 2025 Mar 1;2025(68):56-64. doi: 10.1093/jncimonographs/lgae053.
Cancer care delivery research trials conducted within the National Cancer Institute (NCI) Community Oncology Research Program (NCORP) routinely implement interventions at the practice or provider level, necessitating the use of cluster randomized controlled trials (cRCTs). The intervention delivery requires cluster-level randomization instead of participant-level, affecting sample size calculation and statistical analyses to incorporate correlation between participants within a practice. Practical challenges exist in the conduct of these cRCTs due to unique trial network infrastructures, including the possibility of unequal participant accrual totals and rates and staggered study initiation by clusters, potentially with differences between randomized arms. Execution of cRCT designs can be complex, ie, if some clusters do not accrue participants, unintended cluster-level crossover occurs, how best to identify appropriate cluster-level stratification, timing of randomization, and multilevel eligibility criteria considerations. This article shares lessons learned with potential mitigation strategies from 3 NCORP cRCTs.
在美国国立癌症研究所(NCI)社区肿瘤学研究项目(NCORP)中开展的癌症护理提供研究试验通常在医疗机构或医疗服务提供者层面实施干预措施,因此需要采用整群随机对照试验(cRCT)。干预措施的实施需要进行整群层面的随机分组,而非参与者层面的随机分组,这影响了样本量计算和统计分析,以便纳入医疗机构内参与者之间的相关性。由于独特的试验网络基础设施,在进行这些cRCT时存在实际挑战,包括参与者入组总数和速率可能不平等,以及各整群交错启动研究,随机分组的组间可能存在差异。cRCT设计的执行可能很复杂,例如,如果一些整群没有招募到参与者,就会出现意外的整群层面交叉,如何最好地确定合适的整群层面分层、随机分组时间以及多层面资格标准考量。本文分享了来自3项NCORP cRCT的经验教训及潜在缓解策略。