比较有效性研究试验的适应性设计

John Kairalla, C. Coffey, Mitchell A. Thomann, R. Shorr, K. Muller
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摘要

医疗卫生政策决策者需要改进比较有效性研究(CER)试验的设计和分析方法。在CER试验中,指导初始设计选择的信息可能有限。在一般情况下,自适应设计(ADs)有效地克服了初始信息的限制。然而,CER试验与标准临床试验存在根本差异,包括人群异质性和“最小临床意义差异”的模糊概念。本文的目的是探讨在CER试验背景下使用一种特殊形式的ad来比较治疗。为了达到这个目的,作者回顾了临床CER的现状。他们还确定了CER领域作为新型AD应用的特别强有力的候选者,并说明了两组比较的设计和方法的潜在有用性。作者发现ADs可以稳定电力。此外,设计确保足够的功率,以达到真正的效果,至少在临床显著的预先计划的效应大小,或当变异性大于预期。当真实效果较大或变异性小于计划时,设计允许节省样本量。作者得出结论,CER中的ad具有很大的潜力,可以使试验成功和有效地进行重要的比较。
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Adaptive designs for comparative effectiveness research trials
Abstract Medical and health policy decision-makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a “minimum clinically meaningful difference”. The objective of this article is to explore the use of a particular form of ADs for comparing treatments within the CER trial context. To achieve this, the authors review the current state of clinical CER. They also identify areas of CER as particularly strong candidates for application of novel AD and illustrate the potential usefulness of the designs and methods for two group comparisons. The authors found that ADs can stabilize power. Furthermore, the designs ensure adequate power for true effects are at least at clinically significant pre-planned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned. The authors conclude that ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons.
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