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Abstract：
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.
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.
摘要：
中央核肌病是严重罕见的先天性疾病。这些肌病的临床变异性和遗传异质性导致临床试验设计面临重大挑战。已用于其他罕见疾病的大型安慰剂对照试验的替代策略（例如，使用替代标记或历史对照)具有贝叶斯统计可能解决的局限性。在这里，我们提出了一个贝叶斯模型，该模型使用每个患者自己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。
我们的方法集中于评估6至18岁儿童的1s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,则将患者定义为响应者。如果FEV1反应对应于超过8%的增加,则其被认为是临床相关的。
使用该模型的临床试验的关键终点是反应率。该研究的功效是基于后验概率，即观察到的反应率大于在没有治疗的情况下观察到的反应率，这是根据个体患者的先前自然史预测的。为了适当控制1类错误，应答率差异超过零的阈值概率为91%，确保试验的1类总体错误率为5%。
自然历史数据的贝叶斯统计分析使我们能够可靠地模拟个体患者随时间的症状演变，并将这些模拟轨迹与实际观察到的治疗后结果进行概率比较。所提出的模型充分预测了患者在研究期间的自然演变，并将促进足够强大的试验设计，以应对疾病的稀有性。需要进一步的研究和与监管机构的持续对话，以允许贝叶斯统计在孤儿疾病研究中的更多应用。
我们的方法集中于评估6至18岁儿童的1s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,则将患者定义为响应者。如果FEV1反应对应于超过8%的增加,则其被认为是临床相关的。
使用该模型的临床试验的关键终点是反应率。该研究的功效是基于后验概率，即观察到的反应率大于在没有治疗的情况下观察到的反应率，这是根据个体患者的先前自然史预测的。为了适当控制1类错误，应答率差异超过零的阈值概率为91%，确保试验的1类总体错误率为5%。
自然历史数据的贝叶斯统计分析使我们能够可靠地模拟个体患者随时间的症状演变，并将这些模拟轨迹与实际观察到的治疗后结果进行概率比较。所提出的模型充分预测了患者在研究期间的自然演变，并将促进足够强大的试验设计，以应对疾病的稀有性。需要进一步的研究和与监管机构的持续对话，以允许贝叶斯统计在孤儿疾病研究中的更多应用。
