Identification and Inference for Longitudinal Causal Mediation Analysis in HIV

描述（由申请人提供）：良好的抗逆转录病毒治疗（ART）依从性被广泛认为是持续抑制人类免疫缺陷病毒（HIV）的关键因素，并且通常被认为是维持处方药物治疗方案和最佳患者结果的先决条件。然而，坚持一种治疗方法（相对于另一种治疗方法）导致病毒学失败的程度是复杂的，仍然知之甚少，并且是艾滋病毒研究中一个紧迫的调解问题。了解这一问题在资源贫乏的环境中尤为重要，因为抗逆转录病毒治疗方案选择有限，坚持终身每日多药给药是具有挑战性的，但也是必要的。在这种情况下，量化病毒学失败率的差异在多大程度上是由于治疗依从率的差异造成的，将告知失败率可以通过提高某些抗逆转录病毒疗法的依从率而不是改进抗逆转录病毒疗法本身的方案来改善的程度。这种依从性干预措施在治疗结核病方面非常成功，并被认为对治疗艾滋病毒很重要。不幸的是，传统的中介分析方法假设没有未观察到的混淆的静态设置，因此不适合分析艾滋病毒观察性研究中通常遇到的复杂纵向数据。我们的计划是利用基于潜在结果和图形模型的因果推理的最新发展以及现代半参数理论来弥补这一缺陷。

DESCRIPTION (provided by applicant): Good adherence to anti-retroviral therapy (ART) is widely accepted as a key factor for sustained viral suppression of human immunodeficiency virus (HIV), and is often considered a prerequisite for maintenance on a prescribed drug regimen and optimal patient outcomes. However, the extent to which adherence to a given choice of therapy (relative to another therapy), contributes to virologic failure is complex and stll poorly understood, and is a pressing mediation question in HIV research. Understanding this issue is particularly important in resource poor settings where ART regimen options are limited and adherence to lifelong multi- drug daily dosing is challenging but necessary. In such settings, quantifying to what degree differential rates of virologic failure are due to difference in adherence rates between therapies would inform the extent to which failure rates could be improved by programs that increase adherence rates for certain ARTs, rather than improving ART regimens themselves. Such adherence interventions have been very successful in the treatment of tuberculosis and are considered important in the treatment of HIV. Unfortunately, traditional approaches to mediation analysis assume static settings with no unobserved confounding, and are thus not suited for analysis of complex longitudinal data typically encountered in observational studies of HIV. Our plan is to remedy this deficiency using recent developments in causal inference based on potential outcomes and graphical models, and modern semi-parametric theory.