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支持可推广推断的整群随机试验设计AI 解读

Background: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics to improve trial economy or to support inference about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable…

部分嵌套试验设计中的可推广性分析：坏死性小肠结肠炎外科试验AI 解读

We discuss generalizability analyses under a partially nested trial design, where part of the trial is nested within a cohort of trial-eligible individuals, while the rest of the trial is not nested. This design arises, for example, when only some centers participating in a trial are able to collect data on non-randomized individuals, or when data on non-randomized individuals cannot be collected for the full duration of the trial. Our work is motivated by the Necrotizing…

存在不依从时治疗分配效应推断的推广与迁移AI 解读

We discuss the identifiability of causal estimands for generalizability and transportability analyses, both under perfect and imperfect adherence to treatment assignment. We consider a setting where the trial data contain information on baseline covariates, assignment at baseline, intervention at baseline (point treatment), and outcomes; and where the data from non-randomized individuals only contain information on baseline covariates. In this setting, we review…

将随机试验推断推广至目标总体的全局敏感性分析AI 解读

When individuals participating in a randomized trial differ with respect to the distribution of effect modifiers compared compared with the target population where the trial results will be used, treatment effect estimates from the trial may not directly apply to target population. Methods for extending -- generalizing or transporting -- causal inferences from the trial to the target population rely on conditional exchangeability assumptions between randomized and…

将整群随机试验的因果推断推广至目标总体AI 解读

We describe methods that extend (generalize or transport) causal inferences from cluster randomized trials to a target population of clusters, under a general nonparametric model that allows for arbitrary within-cluster dependence. We propose doubly robust estimators of potential outcome means in the target population that exploit individual-level data on covariates and outcomes to improve efficiency and are appropriate for use with machine learning methods. We illustrate…

在相对效应可迁移假设下识别新目标人群的处理效应AI 解读

Epidemiologists and applied statisticians often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are ``transportable'' across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures (e.g., conditional risk ratios or mean ratios) are transportable from a trial to the target population. We show that transportability for relative…

推广与迁移分析中的亚组效应估计AI 解读

Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific…

多中心试验中的中心特定因果推断AI 解读

In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average over centers may not apply to any of the populations underlying the individual centers. Here, we describe methods for reinterpreting the evidence produced by a multicenter trial in the context of the population underlying each center. We describe how to identify center-specific effects under identifiability conditions that…