依从性的可分离效应
Separable effects for adherence
AI总结：
针对药物依从性差异干扰疗效比较的问题，提出可分离效应估计量，通过修改药物并固定不依从成分消除依从性影响，并给出因果图构建算法与半参数加权估计方法。
摘要：
当对不同药物的依从性存在差异时，比较这些药物会变得复杂。我们可以像通常的因果“符合方案”估计量那样，通过评估持续使用下的有效性来克服依从性问题。然而，当持续使用在实践中难以满足时，该估计量的实用性可能受限。在此，我们提出一类不同的估计量：依从性的可分离效应。这些估计量比较经过修改的药物，同时固定导致不依从的某个组成部分。在关于治疗组成部分作用机制的假设下，可分离效应估计量能够消除依从性差异。这些假设可供领域专家审查，并可使用因果图进行评估。我们描述了一种为可分离效应构建因果图的算法，说明如何使用这些图来推理识别所需的假设，并提供了半参数加权估计量。
英文摘要：
Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in the usual causal `per-protocol' estimand. However, when sustained use is challenging to satisfy in practice, the usefulness of this estimand can be limited. Here we propose a different class of estimands: separable effects for adherence. These estimands compare modified medications, holding fixed a component responsible for non-adherence. Under assumptions about treatment components' mechanisms of effect, the separable effects estimand can eliminate differences in adherence. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
