A causal framework for evaluating the total effect of strategies aiming to expand screening and to improve outcomes
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评估扩大筛查与改善结局策略总效应的因果框架
- 研究问题
- 当策略在群组层面实施、筛查与健康结局位于个体层面，且潜在状态仅对接受筛查者可见时，应如何定义和估计策略总效应？
- 方法与思路
- 作者用反事实分层效应定义目标，处理目标群体成员身份缺失或受干预影响的情况，并扩展两阶段 TMLE 进行识别与估计。
- 材料陈述的贡献
- 摘要称，框架将群组干预、筛查中介和状态缺失纳入总效应分析，并通过模拟展示所提方法的表现及现有方法的局限。
- 阅读关注点
- 核对反事实分层与总效应的精确定义、缺失状态的识别条件，以及两阶段 TMLE 如何处理群组结构、筛查路径和不确定性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出缺失机制、完整识别假设及两阶段估计所需条件。
原文摘要
For many health conditions, there are highly efficacious treatment and prevention products. Maximizing their impact requires strategies that improve the reach of health screening in order to establish who could benefit. For example, HIV prevention strategies aim to expand risk screening and to improve uptake of pre-exposure prophylaxis (PrEP) among those experiencing risk. Often, these strategies induce changes at the group-level (e.g., health clinics or communities) and are evaluated through cluster randomized trials. This scenario creates a complex, multilevel-mediation-missing data problem for the following reasons. First, the strategy is delivered at the cluster-level, while health screening and outcomes are at the individual-level. Second, the strategy improves health outcomes directly and indirectly through improved health screening. Third, everyone has an underlying status, which is only observed among those screened. To formally define the total effect in such settings, we use Counterfactual Strata Effects: causal estimands where the outcome is only relevant for a group whose membership is subject to missingness and/ or impacted by the exposure of interest. To identify and estimate the corresponding statistical estimand, we propose a novel extension of Two-Stage targeted minimum loss-based estimation (TMLE). Simulations demonstrate the practical performance of our approach as well as the limitations of existing approaches.