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Ideal vs. real: a systematic review on handling covariates in randomized controlled trials

机译：理想与实际：系统评价随机对照试验中协变量的处理

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In theory, efficient design of randomized controlled trials (RCTs) involves randomization algorithms that control baseline variable imbalance efficiently, and corresponding analysis involves pre-specified adjustment for baseline covariates. This review sought to explore techniques for handling potentially influential baseline variables in both the design and analysis phase of RCTs. We searched PubMed for articles indexed “randomized controlled trial”, published in the NEJM, JAMA, BMJ, or Lancet for two time periods: 2009 and 2014 (before and after updated CONSORT guidelines). Upon screening (343), 298 articles underwent full review and data abstraction. Typical articles reported on superiority (86%), multicenter (92%), two-armed (79%) trials; 81% of trials involved covariates in the allocation and 84% presented adjusted analysis results. The majority reported a stratified block method (69%) of allocation, and of the trials reporting adjusted analyses, 91% were pre-specified. Trials published in 2014 were more likely to report adjusted analyses (87% vs. 79%, p?=?0.0100) and more likely to pre-specify adjustment in analyses (95% vs. 85%, p?=?0.0045). Studies initiated in later years (2010 or later) were less likely to use an adaptive method of randomization (p?=?0.0066; 7% of those beginning in 2010 or later vs. 31% of those starting before 2000) but more likely to report a pre-specified adjusted analysis (p?=?0.0029; 97% for those initiated in 2010 or later vs. 69% of those started before 2000). While optimal reporting procedures and pre-specification of adjusted analyses for RCTs tend to be progressively more prevalent over time, we see the opposite effect on reported use of covariate-adaptive randomization methods.
机译：从理论上讲，有效设计的随机对照试验（RCT）涉及有效控制基线变量失衡的随机算法，而相应的分析涉及基线协变量的预先调整。这项审查试图探索在RCT的设计和分析阶段中处理潜在影响基线变量的技术。我们在PubMed中搜索了索引为“随机对照试验”的文章，这些文章在NEJM，JAMA，BMJ或Lancet上发布了两个时间段：2009年和2014年（更新的CONSORT准则前后）。筛选（343）后，有298篇文章进行了全面审查和数据抽象。典型文章报道了优越性（86％），多中心（92％），两臂（79％）试验; 81％的试验涉及分配的协变量，而84％的患者提供了调整后的分析结果。大多数报告了分层分层方法（69％），在报告了调整分析的试验中，有91％是预先指定的。 2014年发布的试验更有可能报告调整后的分析结果（87％比79％，p？=？0.0100），更可能预先指定分析中的调整结果（95％vs. 85％，p？=？0.0045）。后来几年（2010年或更晚）开始的研究不太可能使用自适应随机方法（p？=？0.0066; 2010年或更晚开始的研究为7％，2000年之前开始的研究为31％），但更可能采用报告预先指定的调整后分析（p？= 0.0029; 2010年或更晚开始的分析为97％，而2000年之前开始的分析为69％）。尽管随着时间的流逝，最佳的报告程序和针对RCT的调整分析的预先指定趋于逐渐流行，但我们看到对使用协变量自适应随机方法的报道产生了相反的影响。

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