Targeted Learning: Causal Inference Methods for Implementation Science

描述（由申请人提供）：这项竞争性更新将开发一般方法，以评估南部和东部非洲艾滋病毒预防、治疗和护理替代战略的相对有效性。大型集群随机试验和全球队列合作产生了真实世界中数十万患者的纵向数据。这些为开发“基于实践的证据”提供了巨大的资源，这些证据是最大限度地发挥艾滋病毒预防战略的影响和改善医疗保健服务系统所必需的。然而，要实现这一潜力，需要对目标学习领域进行创新，以最大限度地无偏和有效地估计统计参数，最好地接近兴趣的因果效应。首先，必须开发改进的方法来评估患者响应性监测和治疗策略的效果。在常见的强混淆和罕见结果的情况下，目前的估计存在偏差，缺乏效率，对不确定性的测量不可靠。其次，必须为多个时间点的集群和个人干预的联合效应建立一般因果模型和可识别性假设。这些模型将解释集群内个体之间的相互作用和集群之间潜在的污染。这项工作将为社区或诊所内采样集群和测量个人的最佳设计提供信息。第三，必须开发有效且最大程度无偏的估计器，以评估多个时间点上集群和个人水平干预的影响。目前的方法极易受到偏差和误导性推断的影响，这是由于模型规格错误，以及通常错误的假设，即观察到的数据代表了n个独立的、同分布（i.i.d）重复的实验。所开发的方法将阐明基于集群的干预措施影响健康的途径，同时对稀疏性、不规则性和信息缺失、真正独立的单元（集群）很少但可能有数十万个条件独立的单元等共同挑战保持稳健。这些创新的动力来自我们与南部非洲（PI . Egger博士）和东部非洲（PI . Yiannoutsos博士）的国际艾滋病流行病学评估数据库（IeDEA）以及东非社区卫生可持续研究（SEARCH）联盟（PI . Havlir博士）的合作，这是一项随机试验，旨在评估在所有CD4计数下开始抗逆转录病毒治疗的社区范围内的益处。开发的方法将应用于这些数据来源，以调查(i)监测抗逆转录病毒治疗（ART）和指导转向二线方案的策略，（ii）基于社区的艾滋病毒预防策略的直接和间接影响，以及（iii）基于诊所的艾滋病毒护理方案的影响。最后，结果估计器将作为公开可用的软件包和教学论文来实现，以清晰和严格的方式解释方法。

DESCRIPTION (provided by applicant): This competitive renewal will develop general methods for evaluating the comparative effectiveness of alter- native strategies for HIV prevention, treatment and care in Southern and Eastern Africa. Large cluster random- ized trials and global cohort collaborations generate longitudinal data on hundreds of thousands of patients in real world settings. These provide a tremendous resource for developing the "practice-based evidence" needed to maximize the impact of HIV prevention strategies and to improve healthcare delivery systems. Realizing this potential, however, demands innovations to the field of Targeted Learning for maximally unbiased and efficient estimation of statistical parameters, best approximating the causal effects of interest. First, improved methods for estimating the effects of patient responsive monitoring and treatment strategies must be developed. In the common settings of strong confounding and rare outcomes, current estimators suffer from bias, lack efficiency and have unreliable measures of uncertainty. Second, general causal models and identifiability assumptions must be developed for the joint effects of cluster and individual-level interventions over multiple time points. These models will account for interactions between individuals within clusters and potential contamination between clusters. This work will inform the optimal design for sampling clusters and measuring individuals within communities or clinics. Third, efficient and maximally unbiased estimators must be developed to evaluate the impact of cluster and individual-level interventions over multiple time points. Current methods are highly susceptible to bias and misleading inference due to model misspecification and due to the often incorrect assumption that the observed data represent n independent, identically distributed (i.i.d.) repetitions of an experiment. The developed methods will elucidate the pathways by which cluster- based interventions impact health, while remaining robust to the common challenges of sparsity, irregular and informative missingness, and few truly independent units (clusters) but potentially hundreds of thousands of conditionally independent units. These innovations are motivated by our collaborations with the International epidemiologic Databases to Evaluate AIDS (IeDEA) in Southern (PI Dr. Egger) and Eastern Africa (PI Dr. Yiannoutsos) and the Sustain- able East Africa Research in Community Health (SEARCH) consortium (PI Dr. Havlir), a cluster randomized trial to evaluate the community-wide benefits of ART initiation at all CD4 counts. The developed methods will be applied to these data sources to investigate (i) strategies for monitoring antiretroviral therapy (ART) and guiding switches to second line regimens, (ii) the direct and indirect effects of a community-based HIV prevention strategy and (iii) the impact of clinic-based programs for delivering HIV care. Finally, the resulting estimators will be implemented as publicly available software packages and teaching papers written to explain the methodology in a clear and rigorous manner.