论文标题
从补充来源借用以估算主要数据源的因果效应
Borrowing from Supplemental Sources to Estimate Causal Effects from a Primary Data Source
论文作者
论文摘要
数据源的多样性增加为估计治疗，干预或暴露的影响提供了令人兴奋的可能性，特别是如果可以同时使用观察和实验来源的话。源之间的借用可能会导致更有效的估计器，但必须以原则性的方式进行减轻偏见和I型错误。此外，当治疗的影响被混淆时，例如在观测来源或不合规的临床试验中，需要因果效应估计量以同时调整混杂并估算跨数据源的影响。我们考虑了从主要来源估算因果效应并从任何数量的补充来源借用的问题。我们建议使用基于回归的估计器，该估计器基于假设数据源之间的回归系数和参数的交换性。借用是通过多源交换性模型和贝叶斯模型平均实现的。我们通过模拟显示，贝叶斯线性模型和贝叶斯添加剂回归树具有理想的特性，并且在适当的情况下借用。我们将估计量应用于最近完成非常低的尼古丁含量香烟的试验，以调查其对吸烟行为的影响。
The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.
