An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes
An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes
复制标题
DOI：
10.1002/sim.7410
复制
发表时间：
2017-10-30
影响因子：
2
通讯作者：
DeLong, Elizabeth R.
中科院分区：
文献类型：
--
作者：
Li, Fan;Turner, Elizabeth L.;DeLong, Elizabeth R.
分组随机试验是将完整个体组分配到不同比较组的随机研究。采用此类研究设计的一个常见的实际限制是，只有有限数量的组可用，因此，简单的随机化无法充分平衡各组之间的多个组级协变量。因此，提出基于协变量的约束随机化作为一种实现平衡的分配技术。约束随机化涉及生成大量可能的分配方案，计算评估协变量不平衡的平衡分数，将随机化空间限制为候选分配的预先指定百分比，以及随机选择一个方案来实施。当结果是二元时，会出现许多关于此类设计在推理方面的潜在优势的统计问题。特别是，为连续结果发现的属性可能不会直接应用，并且可以使用统计测试的其他变体。在最近两项试验的推动下，我们进行了一系列蒙特卡罗模拟，以评估简单和约束随机化设计下基于模型和基于随机化的测试的统计特性，并进行不同程度的基于分析的协变量调整。我们的结果表明，当在分析中控制预后组级变量并且随机化空间的大小相当小时，约束随机化提高了线性化 F 检验、KC 校正的 GEE t 检验（Kauermann 和 Carroll，2001，美国统计协会杂志 96, 1387-1396）以及两个排列检验的功效。我们还证明，约束随机化可以减少基于冗余分析的非预后协变量调整带来的功率损失。讨论了平衡度量的选择和随机化空间的大小等设计考虑因素。
Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.