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We need to talk about reliability: making better use of test-retest studies for study design and interpretation

机译:我们需要谈论可靠性:更好地利用重测研究进行研究设计和解释

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摘要

Neuroimaging, in addition to many other fields of clinical research, is both time-consuming and expensive, and recruitable patients can be scarce. These constraints limit the possibility of large-sample experimental designs, and often lead to statistically underpowered studies. This problem is exacerbated by the use of outcome measures whose accuracy is sometimes insufficient to answer the scientific questions posed. Reliability is usually assessed in validation studies using healthy participants, however these results are often not easily applicable to clinical studies examining different populations. I present a new method and tools for using summary statistics from previously published test-retest studies to approximate the reliability of outcomes in new samples. In this way, the feasibility of a new study can be assessed during planning stages, and before collecting any new data. An R package called relfeas also accompanies this article for performing these calculations. In summary, these methods and tools will allow researchers to avoid performing costly studies which are, by virtue of their design, unlikely to yield informative conclusions.
机译:除了临床研究的许多其他领域外,神经成像既费时又昂贵,而且可招募的患者稀少。这些限制限制了大样本实验设计的可能性,并经常导致统计不足的研究。结果度量的使用加剧了这个问题,其结果的准确性有时不足以回答所提出的科学问题。通常在使用健康受试者的验证研究中评估可靠性,但是这些结果通常不容易应用于检查不同人群的临床研究。我提出了一种新方法和工具,可以使用以前发布的重测研究中的汇总统计数据来估算新样本中结果的可靠性。这样,可以在计划阶段以及收集任何新数据之前评估一项新研究的可行性。本文还附带了一个名为relfeas的R包,用于执行这些计算。总而言之,这些方法和工具将使研究人员避免进行昂贵的研究,而这些研究由于其设计而无法得出有益的结论。

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