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.
随机化是临床试验中获取公平治疗比较的成熟统计工具。然而，大多数开展不同实验性治疗或单一药物不同剂量早期肿瘤学小型试验的研究者并未对患者进行随机分组。这可能源于传统惯例、医生为患者选择个性化治疗的意愿，或认为随机化在小型试验中价值有限。我们认为，在可行且符合伦理的前提下，随机化在早期试验中极具价值，因为即使样本量较小，它仍能提供公平的治疗比较。本文通过案例说明，在使用独立单臂试验数据比较治疗方法时，可能产生混杂和偏倚。随机化通过消除已知或未知预后变量在组间研究差异对治疗效果的干扰，提供了无偏倚的治疗比较。为促进小型随机试验的设计与分析，我们提出了基于反应率和毒性率的贝叶斯治疗比较标准。同时提供了确定样本量、制定贝叶斯安全性与无效性监测规则、构建平衡随机化方案的实际指导原则。这些方法通过一项治疗类固醇难治性移植物抗宿主病的工程细胞试验得到具体展示。
Practical Bayesian Guidelines for Small Randomized Oncology Trials
肿瘤(癌症)患者之家