Thall Peter F
Department of Biostatistics, MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel). 2025 Jun 7;17(12):1902. doi: 10.3390/cancers17121902.
Randomization is a well-established statistical tool for obtaining fair treatment comparisons in clinical trials. Despite this, most investigators conducting small early-phase oncology trials of different experimental treatments or doses of a single agent do not randomize patients. This may be due to convention, physicians' desire to choose personalized treatments for their patients, or the belief that randomization is of little value in small trials. We argue that, when it is feasible and ethical, randomization is very desirable in early-phase trials because it gives fair treatment comparisons despite the small sample sizes. Illustrations are provided of how confounding and bias may arise when comparing treatments using data from separate single-arm trials. By eliminating confounding treatment effects with between-study differences in known or unknown prognostic variables, randomization provides unbiased treatment comparisons. To facilitate the planning and analysis of small randomized trials, Bayesian criteria for comparing treatments based on response and toxicity rates are provided. Practical guidelines are given for determining sample sizes, specifying Bayesian safety and futility monitoring rules, and constructing a balanced randomization scheme. The methods are illustrated by a trial of engineered cells for treating steroid-refractory graft-versus-host disease.
随机化是一种在临床试验中用于获得公平治疗比较的成熟统计工具。尽管如此，大多数开展不同实验性治疗或单一药物不同剂量的小型早期肿瘤学试验的研究者并未对患者进行随机分组。这可能是由于传统做法、医生希望为患者选择个性化治疗，或者认为随机化在小型试验中价值不大。我们认为，在可行且符合伦理的情况下，早期试验中非常需要进行随机化，因为尽管样本量小，但它能提供公平的治疗比较。文中给出了使用来自单独单臂试验的数据比较治疗时可能出现混杂和偏倚的示例。通过消除已知或未知预后变量的研究间差异导致的混杂治疗效应，随机化提供了无偏的治疗比较。为便于小型随机试验的设计和分析，提供了基于缓解率和毒性率比较治疗的贝叶斯标准。给出了确定样本量、指定贝叶斯安全性和无效性监测规则以及构建平衡随机化方案的实用指南。通过一项使用工程细胞治疗类固醇难治性移植物抗宿主病的试验对这些方法进行了说明。
