A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.

中文翻译：

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存在治疗效果异质性的动态借用。

已经提出了许多统计方法来将补充信息纳入随机临床试验。现有方法经常比较边际治疗效果来评估来源之间的一致性程度。不同的边际治疗效果会导致补充数据的偏差增加或权重降低。这代表了治疗效果异质性存在的局限性，在这种情况下，不同来源之间的边际治疗效果可能仅由于研究人群之间的差异而有所不同。我们引入了协变量调整可交换性的概念，其中边际处理效应的差异可以通过效应调节因子分布的差异来解释。潜在结果框架用于概念化协变量调整和边际可交换性。当边际待遇效果不同但协变量调整的可交换性成立时，我们利用线性模型和现有的多源可交换性模型框架来促进借贷。我们通过模拟研究我们方法的操作特性。我们还使用来自极低尼古丁含量香烟的两项临床试验的数据来说明我们的方法。与仅考虑边际可交换性相比，我们的方法能够在更广泛的情况下合并补充信息。