Selukar Subodh, Prince David K, May Susanne
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Kidney Research Institute, University of Washington, Seattle, WA, USA.
Clin Trials. 2025 Jun;22(3):257-266. doi: 10.1177/17407745241304284. Epub 2025 Jan 2.
N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.
In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.
We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a -value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.
Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.
单病例试验比较单个参与者的两种或更多治疗方案。这些试验已被用于研究关节炎和注意力缺陷多动障碍等慢性病的治疗方案。此外，有人建议将其作为研究罕见人群干预措施的一种手段，这些人群可能难以纳入标准临床试验，例如需要器官移植的HIV阳性患者的治疗方案。在传统临床试验中，对累积数据的序贯监测已得到充分研究，但这些方法尚未在单病例试验中实施。然而，在单病例试验中提前有效终止试验的选择可以更快地做出决策，直接改善患者健康。
在这项工作中，我们提出并评估一个框架，以（1）促进对具有连续结果的单个单病例试验进行序贯监测，以及（2）整合一系列已完成的序贯监测单病例试验的结果。通过采用单病例试验常见的区组结构，我们建议当使用线性混合效应模型分析一个单病例试验的数据时，可以采用现有的序贯监测方法。为了整合一系列已完成的序贯监测单病例试验的结果，我们建议在随机效应模型中采用逆方差加权法合并各组成试验的朴素估计值。我们通过模拟评估这些建议。
我们发现，对于计划区组数量较少的单病例试验，Ⅰ类错误可能会大幅增加，但对于计划区组较多、每个区组周期数较多的试验，或者通过使用P值校正，Ⅰ类错误可以达到名义水平。对于那些Ⅰ类错误可接受的情况，序贯监测与无序贯监测的试验相比，具有相似的检验效能，且平均而言停止试验更早。并且，正如预期的那样，我们发现，在一个系列中纳入更多的组成试验会降低合并点估计量的均方误差。
在适当的设计考虑下，我们提出的序贯监测框架可以支持临床医生平均而言更早地为参与单病例试验的患者提供重要决策。
