Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment. Data from both stages are used to determine the efficacy of the active treatment. We present a Bayesian approach where information is borrowed between stage 1 and stage 2. We compare our approach to standard methods using only stage 1 data and a log‐linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that the Bayesian method has smaller root‐mean‐square‐error and 95% credible interval widths than standard methods in the tested scenarios. We conclude that it is advantageous to utilize data from both stages for a primary efficacy analysis and that the specific snSMART design shown here can be used in the registration of a drug for the treatment of rare diseases.

中文翻译：

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贝叶斯方法在小型、连续、多重分配、随机试验中比较剂量水平与安慰剂

由于符合试验条件的患者数量有限，研究罕见疾病治疗方法的临床试验的设计和实施具有挑战性。用于应对这一挑战的一种设计是小 n、顺序、多重分配、随机试验 (snSMART)。我们提出了一种新的 snSMART 设计，该设计研究了与安慰剂相比，在低剂量和高剂量下测试的药物的反应率。患者被随机分配到初始治疗（第 1 阶段）。在第 2 阶段，根据他们的初始治疗和他们对第 1 阶段治疗的反应，患者被随机分配到相同或不同剂量的治疗。来自两个阶段的数据用于确定积极治疗的功效。我们提出了一种贝叶斯方法，其中在阶段 1 和阶段 2 之间借用信息。我们将我们的方法与仅使用第 1 阶段数据的标准方法和使用来自两个阶段的数据的对数线性泊松模型进行比较，其中使用广义估计方程估计参数。我们观察到贝叶斯方法在测试场景中比标准方法具有更小的均方根误差和 95% 的可信区间宽度。我们得出结论，利用这两个阶段的数据进行主要疗效分析是有利的，并且此处显示的特定 snSMART 设计可用于注册治疗罕见疾病的药物。我们观察到贝叶斯方法在测试场景中比标准方法具有更小的均方根误差和 95% 的可信区间宽度。我们得出结论，利用这两个阶段的数据进行主要疗效分析是有利的，并且此处显示的特定 snSMART 设计可用于注册治疗罕见疾病的药物。我们观察到贝叶斯方法在测试场景中比标准方法具有更小的均方根误差和 95% 的可信区间宽度。我们得出结论，利用这两个阶段的数据进行主要疗效分析是有利的，并且此处显示的特定 snSMART 设计可用于注册治疗罕见疾病的药物。