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Some practical problems in implementing randomization.

机译:实施随机化时的一些实际问题。

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BACKGROUND: While often theoretically simple, implementing randomization to treatment in a masked, but confirmable, fashion can prove difficult in practice. PURPOSE: At least three categories of problems occur in randomization: (1) bad judgment in the choice of method, (2) design and programming errors in implementing the method, and (3) human error during the conduct of the trial. This article focuses on these latter two types of errors, dealing operationally with what can go wrong after trial designers have selected the allocation method. RESULTS: We offer several case studies and corresponding recommendations for lessening the frequency of problems in allocating treatment or for mitigating the consequences of errors. Recommendations include: (1) reviewing the randomization schedule before starting a trial, (2) being especially cautious of systems that use on-demand random number generators, (3) drafting unambiguous randomization specifications, (4) performing thorough testing before entering a randomization system into production, (5) maintaining a dataset that captures the values investigators used to randomize participants, thereby allowing the process of treatment allocation to be reproduced and verified, (6) resisting the urge to correct errors that occur in individual treatment assignments, (7) preventing inadvertent unmasking to treatment assignments in kit allocations, and (8) checking a sample of study drug kits to allow detection of errors in drug packaging and labeling. LIMITATIONS: Although we performed a literature search of documented randomization errors, the examples that we provide and the resultant recommendations are based largely on our own experience in industry-sponsored clinical trials. We do not know how representative our experience is or how common errors of the type we have seen occur. CONCLUSIONS: Our experience underscores the importance of verifying the integrity of the treatment allocation process before and during a trial. Clinical Trials 2010; 7: 235-245. http://ctj.sagepub.com.
机译:背景:虽然从理论上讲通常很简单,但在实践中证明以掩盖但可确认的方式实施随机化治疗可能会很困难。目的:随机分配中至少会出现三类问题:(1)方法选择时的错误判断;(2)在实施该方法时出现设计和编程错误;以及(3)试验进行中的人为错误。本文重点介绍后两种类型的错误,从操作上处理在试验设计者选择分配方法之后可能出现的问题。结果:我们提供了一些案例研究和相应的建议,以减少分配治疗时出现问题的频率或减轻错误的后果。建议包括:(1)在开始试验之前检查随机化时间表,(2)特别注意使用按需随机数发生器的系统,(3)起草明确的随机化规范,(4)在进入随机化之前进行彻底的测试系统投入生产后,(5)维护一个数据集,该数据集将捕获用于随机化参与者的调查员的值,从而允许重现和验证治疗分配的过程,(6)抵制纠正个别治疗分配中出现的错误的冲动,( 7)防止在药盒分配中不加掩盖地揭露治疗方案,以及(8)检查研究药物盒的样品,以检测药物包装和标签中的错误。局限性:尽管我们对文献记载的随机化错误进行了文献检索,但我们提供的示例和所产生的建议很大程度上基于我们自己在行业赞助的临床试验中的经验。我们不知道我们的经验是多么具有代表性,或者我们所看到的类型的常见错误是如何发生的。结论:我们的经验强调了在试验之前和期间验证治疗分配过程完整性的重要性。 2010年临床试验; 7:235-245。 http://ctj.sagepub.com。

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