Targeted Learning: Causal Inference Methods for Implementation Science

项目总结 这种竞争性的更新将发展出评估Alter-Rate的比较有效性的一般方法。 南部和东部非洲艾滋病毒预防、治疗和护理的本地战略。大型星团随机- 标准化试验和全球队列协作产生了关于数十万名患者的纵向数据 真实世界的设置。这些都为开发所需的“基于实践的证据”提供了巨大的资源 以最大限度地发挥艾滋病毒预防战略的影响，并改进医疗保健提供系统。意识到这一点 然而，潜力要求在定向学习领域进行创新，以实现最大限度的公正和高效 统计参数的估计，最好地逼近感兴趣的因果效应。 第一，改进了评估患者反应监测和治疗策略的效果的方法 必须被开发。在强混杂和罕见结果的常见环境中，当前的估计者受到影响 从偏颇、缺乏效率和有不可靠的不确定性衡量。第二，一般因果模型和 必须为集群干预和个人干预的联合影响制定可辨识性假设 在多个时间点上。这些模型将考虑集群内个体之间的相互作用和 星系团之间的潜在污染。这项工作将为抽样集群的最优设计和 测量社区或诊所内的个人。第三，有效且最大限度无偏的估计者必须 为评估群组和个人一级的干预措施在多个时间点的影响而制定。 当前的方法由于模型的错误说明和错误说明而高度容易受到偏差和误导性推理的影响 由于经常错误地假设观测数据代表n个独立的、同分布的 (I.I.D.)实验的重复。所开发的方法将阐明集群- 以健康为基础的干预措施影响健康，同时保持对稀疏、不规则和 信息缺失，真正独立的单元(集群)很少，但可能有数十万个 有条件的独立单位。 这些创新的动力来自于我们与国际流行病学数据库的合作 评估南部(Pi Egger博士)和东部非洲(Pi Dr.Yannoutsos)的艾滋病(IeDEA)及其持续- 有能力的东非社区卫生研究(SEARCH)联盟(Pi Dr.Havlir)，随机分组 评估启动抗逆转录病毒疗法在所有CD4计数下的社区范围效益的试验。已开发的方法 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)的策略 和指导改用二线疗法，(2)社区艾滋病毒的直接和间接影响 预防战略和(3)以诊所为基础的艾滋病毒护理方案的影响。最后，由此产生的 评估者将作为公开可用的软件包和编写的教学论文来实施，以解释 方法论清晰、严谨。

PROJECT SUMMARY 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.