Division of Child Neurology, Centre de Référence Des Maladies Neuromusculaires, Department of Paediatrics, University Hospital of Liège and University of Liège, Liège, Belgium.
Pharmalex Belgium, Mont-Saint-Guibert, Belgium.
Orphanet J Rare Dis. 2021 Jan 6;16(1):3. doi: 10.1186/s13023-020-01663-7.
Centronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes.
Our approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%.
The key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial.
Bayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.
先天性中轴肌营养不良症是严重的罕见先天性疾病。这些肌营养不良症的临床表现变异性和遗传异质性导致临床试验设计面临重大挑战。在其他罕见疾病中使用的替代大型安慰剂对照试验的策略（例如，使用替代标志物或历史对照）存在局限性，贝叶斯统计学可能会解决这些局限性。在这里，我们提出了一种贝叶斯模型，该模型使用每个患者自身的自然史研究数据来预测在没有治疗的情况下的进展。这项前瞻性多中心自然史研究评估了 59 名携带 MTM1 或 DNM2 基因突变的患者的 4 年随访数据。
我们的方法侧重于评估 6 至 18 岁儿童的 1 秒用力呼气量（FEV1）。如果在治疗后观察到改善，并且在没有干预的情况下这种改善的预测概率小于 0.01，则将患者定义为应答者。如果 FEV1 反应对应于超过 8%的增加，则认为其具有临床相关性。
使用该模型进行临床试验的关键终点是反应率。研究的效力基于观察到的反应率大于根据个体患者以前的自然史预测的无治疗情况下观察到的反应率的后验概率。为了适当控制 I 型错误，将响应率差异超过零的阈值概率适应到 91%，从而确保试验的总体 I 型错误率为 5%。
对自然史数据进行贝叶斯统计分析，使我们能够可靠地模拟随时间推移单个患者症状的演变，并概率性地将这些模拟轨迹与实际观察到的治疗后结果进行比较。所提出的模型充分预测了患者在研究期间的自然演变，并将有助于设计足够强大的试验，可以应对疾病的罕见性。需要进一步研究和与监管机构的持续对话，以使贝叶斯统计学在孤儿病研究中得到更多应用。
