Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call “partial empirical Bayes.” We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

中文翻译：

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使用群体交叉试验来改进 N-of-1 试验中有关治疗个体化的决策过程


医疗保健研究人员对利用 N-of-1 临床试验进行个体化药物治疗表现出了新的兴趣。在这里，我们提出了一种在 N-of-1 试验中进行个体化治疗的频率论方法，我们称之为“部分经验贝叶斯”。我们将先前进行的人群交叉试验提供的信息与个体患者数据相结合，推断出对患者最有益的治疗方法。我们提出了一种估计最佳治疗周期数的方法，并调查了 N-of-1 试验比传统临床方法更有益的统计条件。我们用随机系数线性模型代表患者群体，并计算治疗后个体疾病严重程度的估计量。我们展示了最常见的 N-of-1 设计下估计量的一致性，并通过少量患者的反应检查其预测误差和性能。我们通过模拟新患者证明，我们的方法相当于或优于为所有患者推荐平均最佳治疗的常见临床实践和简单比较测试治疗的平均反应的常见个体化方法。我们的结论是，在某些情况下，N-of-1 试验的个体化是非常有益的，而在其他情况下，个体化可能没有效果。