集群随机对照试验的实际设计考虑:社区瘤学研究中学到的经验教训
Emily V Dressler1, Stephanie L Pugh2, Heather J Gunn3
1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Journal of the National Cancer Institute. Monographs
|February 24, 2025
概括
在癌症护理研究中的集群随机对照试验 (cRCT) 面临着独特的挑战. 本研究分享了在国家癌症研究所社区瘤学研究计划 (NCORP) 中进行这些复杂试验的实际经验教训和缓解策略.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 医疗保健服务研究 医疗服务研究
背景情况:
- 癌症护理研究通常使用集群随机对照试验 (cRCT) 来评估实践或提供者级干预措施.
- 国家癌症研究所社区瘤研究计划 (NCORP) 网络经常使用cRCT,需要集群级别而不是参与者级别的随机化.
- 集群级随机化影响样本大小计算和统计分析,因为集群内部的相关性.
研究的目的:
- 识别和解决在NCORP网络中进行cRCT时遇到的实际挑战.
- 在NCORPcRCTs中分享所学到的经验教训,并提出NCORPcRCTs中常见问题的缓解策略.
- 用cRCTs改进未来癌症护理研究的设计和执行.
主要方法:
- 这项研究基于三个特定的NCORP集群随机对照试验 (cRCT) 的经验.
- 它考察了与试验网络基础设施,参与者积累和研究启动相关的实际挑战.
- 讨论了对集群层次分层,随机化时间和多层次资格标准的考虑.
主要成果:
- 不平等的参与者积累和跨集群的分阶段研究启动构成了重大挑战.
- 无意的集群级交叉和一些集群中参与者的非积累使试验执行复杂化.
- 确定最佳的集群级分层和随机化时间需要仔细规划.
结论:
- 在癌症治疗研究中进行cRCT需要解决独特的后勤和统计复杂性.
- 积极的战略对于缓解与参与者积累,随机化和网络基础设施相关的挑战至关重要.
- 从这些NCORP试验中吸取的教训可以为未来癌症研究网络的设计和实施提供信息.
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