Division of Biostatistics and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Suite 303, MSC 835, Charleston, SC 29425-8350, USA.
Contemp Clin Trials. 2011 Mar;32(2):250-9. doi: 10.1016/j.cct.2010.11.005. Epub 2010 Nov 13.
Motivated by potentially serious imbalances of continuous baseline covariates in clinical trials, we investigated the cost in statistical power of ignoring the balance of these covariates in treatment allocation design for a logistic regression model. Based on data from a clinical trial of acute ischemic stroke treatment, computer simulations were used to create scenarios varying from the best possible baseline covariate balance to the worst possible imbalance, with multiple balance levels between the two extremes. The likelihood of each scenario occurring under simple randomization was evaluated. The power of the main effect test for treatment was examined. Our simulation results show that the worst possible imbalance is highly unlikely, but it can still occur under simple random allocation. Also, power loss could be nontrivial if balancing distributions of important continuous covariates were ignored even if adjustment is made in the analysis for important covariates. This situation, although unlikely, is more serious for trials with a small sample size and for covariates with large influence on primary outcome. These results suggest that attempts should be made to balance known prognostic continuous covariates at the design phase of a clinical trial even when adjustment is planned for these covariates at the analysis.
受到临床试验中连续基线协变量潜在严重失衡的启发，我们研究了在逻辑回归模型的治疗分配设计中忽略这些协变量平衡的统计功效代价。基于急性缺血性脑卒中治疗临床试验的数据，我们使用计算机模拟创建了从最佳可能的基线协变量平衡到最差可能的失衡的场景，在这两个极端之间有多个平衡水平。评估了简单随机化下每个场景发生的可能性。检验了治疗主效应检验的功效。我们的模拟结果表明，最坏的可能失衡虽然不太可能，但在简单随机分配下仍可能发生。即使在分析中对重要协变量进行调整，忽略重要连续协变量的分布平衡也可能导致相当大的功效损失。这种情况虽然不太可能，但对于样本量较小的试验和对主要结局影响较大的协变量更为严重。这些结果表明，即使在分析中计划对这些协变量进行调整，也应该在临床试验的设计阶段尝试平衡已知的预后连续协变量。
