Identification and Inference for Longitudinal Causal Mediation Analysis in HIV

说明(申请人提供)：良好坚持抗逆转录病毒疗法(ART)被广泛认为是持续抑制人类免疫缺陷病毒(HIV)病毒的关键因素，通常被认为是维持处方药物方案和最佳患者结果的先决条件。然而，坚持一种特定的治疗方法(相对于另一种治疗方法)在多大程度上导致病毒学失败是复杂的，仍然知之甚少，这是艾滋病毒研究中一个紧迫的中介问题。在资源匮乏的情况下，理解这个问题尤其重要，因为ART方案的选择有限，坚持终身多药每日给药具有挑战性，但也是必要的。在这种情况下，量化不同疗法之间的依从率差异导致的病毒学失败率在多大程度上会告诉我们，通过提高某些ART的依从率的计划，而不是改善ART方案本身，失败率可以在多大程度上得到改善。这种坚持干预措施在治疗结核病方面非常成功，被认为是治疗艾滋病毒的重要手段。不幸的是，传统的调解分析方法假设没有未观察到的混淆的静态环境，因此不适合分析在艾滋病毒观察性研究中通常遇到的复杂的纵向数据。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不足。

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.