Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
PLoS One. 2020 Feb 4;15(2):e0228077. doi: 10.1371/journal.pone.0228077. eCollection 2020.
N-of-1 trials allow inference between two treatments given to a single individual. Most often, clinical investigators analyze an individual's N-of-1 trial data with usual t-tests or simple nonparametric methods. These simple methods do not account for serial correlation in repeated observations coming from the individual. Existing methods accounting for serial correlation require simulation, multiple N-of-1 trials, or both. Here, we develop t-tests that account for serial correlation in a single individual. The development includes effect size and precision calculations, both of which are useful for study planning. We then use Monte Carlo simulation to evaluate statistical properties of these serial t-tests, namely, Type I and II errors, and confidence interval widths, and compare these statistical properties to those of analogous usual t-test. The serial t-tests clearly outperform the usual t-tests commonly used in reporting N-of-1 results. Examples from N-of-1 clinical trials in fibromyalgia patients and from a behavioral health setting exhibit how accounting for serial correlation can change inferences. These t-tests are easily implemented and more appropriate than simple methods commonly used; however, caution is needed when analyzing only a few observations.
N-of-1 试验允许对单个个体接受的两种治疗进行推断。大多数情况下,临床研究人员使用常用的 t 检验或简单的非参数方法分析个体的 N-of-1 试验数据。这些简单的方法没有考虑来自个体的重复观察的序列相关性。现有的考虑序列相关性的方法需要模拟、多个 N-of-1 试验或两者兼而有之。在这里,我们开发了考虑单个个体中序列相关性的 t 检验。该开发包括效应大小和精度计算,两者对于研究计划都很有用。然后,我们使用蒙特卡罗模拟来评估这些串行 t 检验的统计特性,即 I 型和 II 型错误以及置信区间宽度,并将这些统计特性与类似的常用 t 检验进行比较。串行 t 检验明显优于通常用于报告 N-of-1 结果的 t 检验。纤维肌痛患者的 N-of-1 临床试验和行为健康环境中的示例展示了如何改变对序列相关性的推断。这些 t 检验易于实施,比常用的简单方法更合适;但是,当仅分析几个观察结果时需要谨慎。