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固定数据收集期的基于交叉设计的N-of-1临床试验中的效能与设计问题

Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods.

作者信息

Wang Yanpin, Schork Nicholas J

机构信息

Model Risk Management, USAA, 9257 Delaney Creek Blv, Tampa, FL 33619, USA.

Quantitative Medicine and Systems Biology, The Translational Genomics Research Institute (TGen), an affiliate of the City of Hope National Medical Center, 445 North Fifth Street, Phoenix, AZ 85004, USA.

出版信息

Healthcare (Basel). 2019 Jul 2;7(3):84. doi: 10.3390/healthcare7030084.

Abstract

"N-of-1," or single subject, clinical trials seek to determine if an intervention strategy is more efficacious for an individual than an alternative based on an objective, empirical, and controlled study. The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. The effect of serial correlation between measurements, the number of evaluation periods, the use of washout periods, heteroscedasticity (i.e., unequal variances among responses to the interventions) and intervention-associated carry-over phenomena on the power of such studies is crucially important for putting the yield and feasibility of N-of-1 trial designs into context. We evaluated the effect of these phenomena on the power of different designs for N-of-1 trials using analytical theory based on standard likelihood principles assuming an autoregressive lag 1, i.e., AR(1), serial correlation structure among the measurements as well as simulation studies. By evaluating the power to detect effects in many different settings, we show that the influence of serial correlation and heteroscedasticity on power can be substantial, but can also be mitigated to some degree through the use of appropriate multiple evaluation periods. We also show that the detection of certain types of carry-over effects can be heavily influenced by design considerations as well.

摘要

“单病例”或单受试者临床试验旨在通过一项客观、实证且受控的研究,确定一种干预策略对个体而言是否比另一种干预策略更有效。此类试验的设计通常基于一种简单的交叉策略,设有多个干预反应评估期。测量值之间的序列相关性、评估期的数量、洗脱期的使用、异方差性(即对干预的反应中存在不等方差)以及与干预相关的残留现象对这类研究效能的影响,对于正确认识单病例试验设计的产出和可行性至关重要。我们基于标准似然原理,采用分析理论,并假设测量值之间存在自回归滞后1(即AR(1))序列相关结构,同时结合模拟研究,评估了这些现象对不同单病例试验设计效能的影响。通过评估在多种不同情况下检测效应的效能,我们发现序列相关性和异方差性对效能的影响可能很大,但通过使用适当数量的评估期,在一定程度上也可以减轻这种影响。我们还表明,某些类型残留效应的检测也会受到设计因素的严重影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67f6/6787650/306ef47ea8bd/healthcare-07-00084-g001.jpg

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