Université Paris-Saclay, Université Paris-Sud, UVSQ, CESP, INSERM, F-94085, Villejuif, France.
Service de biostatistique et d'épidémiologie, Gustave Roussy, Université Paris-Saclay, F-94805, Villejuif, France.
BMC Med Res Methodol. 2019 Apr 24;19(1):85. doi: 10.1186/s12874-019-0714-z.
Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial's analysis through a prior distribution.
Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions.
The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data.
In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.
对于罕见病的新治疗方法进行有力的随机对照试验（RCT）通常是不可行的。然而，随着历史数据的可用性不断增加，将现有信息纳入样本量较小的试验中以增加功效是很有吸引力的。贝叶斯方法可以通过先验分布将历史数据纳入试验分析中。
受一项旨在评估米伐木肽对骨肉瘤患者无事件生存影响的 RCT 的启发，我们进行了一项模拟研究，以评估将历史个体对照数据和汇总治疗效果估计值纳入试验分析对试验操作特征的影响。我们使用来自历史个体对照数据的功效先验来为威布尔和分段指数模型的基线参数赋值，而使用混合先验来总结关于相对治疗效果的汇总信息。我们评估了先验数据冲突对参数和生存模型的影响，这些冲突是针对先验分布中分配给历史信息的一组预定义权重。
操作特征根据分配给每个历史信息源的权重、混合先验中信息性和模糊性成分的方差以及历史数据和新数据之间的一致性程度而有所不同。当历史对照和新对照遵循不同的生存分布时，我们没有观察到在新试验分析中选择分段指数模型相对于威布尔模型的任何优势。然而，鉴于新数据的生存分布形状通常会存在不确定性，我们认为这仍然很有吸引力。
在 Sarcome-13 试验的背景下，以及其他在罕见病中的类似研究中，通过纳入与新试验数据一致的不同类型的历史信息来提高功效和准确性的收益，必须与数据不一致时可能导致的估计偏差和可能的功效损失相平衡。必须根据这种权衡来仔细选择分配给历史数据的权重。需要进一步的模拟研究来调查纳入历史数据的方法，以推广这些发现。
Zotero 插件
