Evaluating hybrid controls methodology in early-phase oncology trials: a simulation study based on the MORPHEUS-UC trial
早期肿瘤试验混合对照方法评估：基于MORPHEUS-UC的模拟研究AI 解读 ↓
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早期肿瘤试验混合对照方法评估：基于MORPHEUS-UC的模拟研究
- 研究问题
- 在小样本早期肿瘤试验中，将当前对照与历史试验对照结合，能否提高治疗效应估计精度，并应对混杂、模型错设和跨试验异质性？
- 方法与思路
- 作者基于当前试验与两个历史试验的数据设置模拟，比较采用Cox或Weibull模型的频率学方法及不同先验下的贝叶斯动态借用。
- 材料陈述的贡献
- 摘要称在所考察情形下，联合频率学方法及不调整协变量、采用非信息先验的贝叶斯方案更受支持，尤其当跨试验治疗效应异质时。
- 阅读关注点
- 应核对历史对照与当前试验的可比性、未测混杂和异质性的模拟设定、边际风险比定义，以及方法取舍所依据的性能指标。
- 阅读边界
- 这是预印本，不能据此认定已同行评审或期刊接收。推荐依赖所设模拟情景，摘要未给出性能数值，作者也要求更多模拟支持推广。
原文摘要
Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase the statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings we recommend more simulation studies.