Dynamic wait-listed designs for randomized trials:: new designs for prevention of youth suicide
Dynamic wait-listed designs for randomized trials:: new designs for prevention of youth suicide
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DOI：
10.1191/1740774506cn152oa
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发表时间：
2006-01-01
期刊：
影响因子：
2.7
通讯作者：
Pena, Juan
中科院分区：
文献类型：
--
作者：
Brown, C. Hendricks;Wyman, Peter A.;Pena, Juan
传统的等候名单设计，其中一半被随机分配到早期接受干预，另一半被随机分配到稍后接受干预，通常被那些不愿意接受无治疗组的社区所接受。因此，这种传统的等待列表设计为评估干预措施的短期影响提供了极好的机会。我们引入了一类新的等待列表设计进行随机实验，所有受试者接受干预，干预的时间是随机分配的。我们使用术语“动态等待列表设计”来描述这种新类型。目的本文研究了一类新的统计设计，其中随机分配干预条件在试验中多次发生。作为传统等候名单设计的延伸，这种动态设计允许所有受试者在随机时间接受干预。我们在一项正在进行的以学校为基础的试验中寻找提高统计能力的动力，该试验正在测试一个看门人培训计划，以识别有自杀倾向的青少年并将他们转介给治疗，这种新的设计课程在主要结果是计数或发生率时特别有用，例如自杀行为，其发生率可能因不受控制的因素而随时间波动。方法在允许潜在发生率非系统变化的条件下，计算各种动态待定设计的统计功率。我们还提出了一个正在进行的大型试验的例子，以评估在32所学校进行的看门人培训自杀预防计划，我们最初是作为一个经典的随机等待列表设计开始的。本研究的主要目的是统计被学校系统认定为有自杀想法或行为的儿童的数量，这些儿童随后被社区的心理健康专家证实为有自杀倾向。结果总体结果表明，动态待售设计总是比传统待售设计具有更高的统计能力。这种功率的增加是相当可观的。在干预效果小、速率随时间变化很大的情况下，很容易获得33%的效率增益。当某一结果的速率变化非常低或干预效果很大时，效率增益接近100%。随机分配发生的次数从2次（对于标准的等待列表设计）到4次的少量增加可以大大减少方差。当将标准的等待列表设计转换为研究中途的动态设计时，效率收益也很高。与所有待定设计一样，动态待定设计只能用于评估短期影响。由于所有受试者最终都接受了干预，因此在随机分配期结束后无法进行比较。统计能力的好处主要局限于可以作为计数或事件时间数据处理的结果。动态设计随机分配单位-个人或团体-在研究过程中的不同时间开始干预。这种设计有助于测试筛查新病例或现有病例的干预措施，以及测试干预措施在整个系统范围内传播或扩展时的可扩展性。在外生因素存在的情况下，它们可以在统计能力和稳健性方面改进传统的等候名单设计。本文证明，这种设计产生更小的标准误差，并能实现更高的统计功率比标准的等待列表设计。同样重要的是，动态设计还可以帮助减少大规模实施干预措施的后勤挑战。当干预需要在整个研究过程中分配大量的培训资源时，动态等候名单设计可能会增加培训率，并导致更高水平的计划实施。
Background The traditional wait-listed design, where half are randomly assigned to receive the intervention early and half are randomly assigned to receive it later, is often acceptable to communities who would not be comfortable with a no-treatment group. As such this traditional wait-listed design provides an excellent opportunity to evaluate short-term impact of an intervention. We introduce a new class of wait-listed designs for conducting randomized experiments where all subjects receive the intervention, and the timing of the intervention is randomly assigned. We use the term "dynamic wait-listed designs" to describe this new class.Purpose This paper examines anew class of statistical designs where random assignment to intervention condition occurs at multiple times in a trial. As an extension of a traditional wait-listed design, this dynamic design allows all subjects to receive the intervention at a random time. Motivated by our search for increased statistical power in an ongoing school-based trial that is testing a program of gatekeeper training to identify suicidal youth and refer them to treatment, this new design class is especially useful when the primary outcome is a count or rate of occurrence, such as suicidal behavior, whose rate can fluctuate over time due to uncontrolled factors.Methods Statistical power is computed for various dynamic wait-listed designs under conditions where the underlying rate of occurrence is allowed to vary nonsystematically. We also present as an example a large ongoing trial to evaluate a gatekeeper training suicide prevention program in 32 schools which we initially began as a classic randomized wait-listed design. The primary outcome of interest in this study is the count of the number of children who are identified by the school system as having suicidal thoughts or behaviors who are then validated as being suicidal by mental health professionals in the community.Results A general result shows that dynamic wait-listed designs always have higher statistical power over a traditional wait-listed design. This power increase can be substantial. Efficiency gains of 33% are easy to obtain for situations where the intervention has a small effect and the variation in rate across time is quite high. When the rate variation for an outcome is very low or the intervention effect is large, efficiency gains approach 100%. A small increase in the number of times where random assignment occurs from 2 - for the standard wait-listed design, to say 4 can provide a large reduction in variance. Efficiency gains can also be high when converting standard wait-listed design to a dynamic one half-way into the study.Limitations As with all wait-listed designs, dynamic wait-listed designs can only be used to evaluate short-term impact. Since all subjects eventually receive the intervention, no comparison can be made after the end of the random assignment period. The statistical power benefits are primarily limited to outcomes that can be treated as count or time to event data.Conclusions A dynamic design randomly assigns units - either individuals or groups - to start the intervention at varying times during the course of the study. This design is useful in testing interventions that screen for new or existing cases, as well as testing the scalability of interventions as they are disseminated or expanded system wide. They can improve on the traditional wait-listed design both in terms of statistical power and robustness in the presence of exogenous factors. This paper demonstrates that such designs yield smaller standard errors and can achieve higher statistical power than that of a standard wait-listed design. Just as important, dynamic designs can also help reduce the logistical challenges of implementing an intervention on a wide scale. When the intervention requires that significant training resources be allocated throughout the study, the dynamic wait-listed design is likely to increase the rate of training and lead to a higher level of program implementation.