Targeted Learning: Causal Inference Methods for Implementation Science

有针对性的学习：实现科学的因果推理方法

• 批准号: 8659000
• 负责人: Mark J Vanderlaan
• 金额: $ 46.19万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8659000
• 关键词: AIDS prevention; Accounting; Acquired Immunodeficiency Syndrome; Address; Africa; Algorithms; Anti-Retroviral Agents; CD4 Lymphocyte Count; Caring; Clinic; Clinical; Collaborations; Communities; Community Health; Complex; Computer software; Data; Data Sources; Databases; Eastern Africa; Educational process of instructing; Enrollment; Epidemiology; Evidence based practice; Failure; Funding; Goals; Grant; HIV; Health; Individual; International; Intervention; Investigation; Joints; Kenya; Learning; Link; Measures; Mediating; Mediation; Methodology; Methods; Modeling; Monitor; Nurses; Observational Study; Outcome; Paper; Pathway interactions; Patients; Prevention strategy; Randomized; Recording of previous events; Regimen; Research; Research Design; Research Personnel; Resources; Risk; Sampling; Science; Series; Southern Africa; Specific qualifier value; Structural Models; System; Testing; Time; Triage; Uganda; Uncertainty; Work; Writing; antiretroviral therapy; base; cohort; comparative effectiveness; design; health care delivery; implementation science; improved; innovation; interest; mortality; novel; programs; public health relevance; randomized trial; research study; response; software development; theories; tool; treatment strategy

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.

项目摘要 这种竞争性的更新将制定一般的方法来评估比较有效的改变， 南部和东部非洲艾滋病毒预防、治疗和护理的本地战略。大集群随机- 化试验和全球队列合作产生了数十万患者的纵向数据， 真实的世界设置。这些为开发所需的“基于实践的证据”提供了巨大的资源 最大限度地发挥艾滋病毒预防战略的影响，并改善保健服务系统。实现这一 然而，潜在的，要求创新的领域，有针对性的学习，以最大限度地公正和有效的 统计参数的估计，最接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效果的方法 必须加以发展。在常见的强混杂和罕见结果的情况下，目前的估计受到影响， 缺乏效率，对不确定性的测量不可靠。第二，一般因果模型和 必须为集群和个人层面干预的联合效应制定可识别性假设 在多个时间点。这些模型将考虑集群内个体之间的相互作用， 集群之间的潜在污染。这项工作将为抽样集群的最佳设计提供信息， 测量社区或诊所内的个体。第三，有效和最大无偏估计必须 制定一个评价多个时间点集群和个人一级干预措施的影响的指标。 由于模型的错误设定，目前的方法非常容易受到偏见和误导性推断的影响， 由于经常不正确的假设，即观察到的数据表示n个独立的、同分布的 （一日一次）重复一个实验。所开发的方法将阐明集群- 基于干预措施的干预措施会影响健康，同时仍能应对稀疏、不规则和 信息缺失，很少有真正独立的单位（集群），但可能有数十万个 条件独立的单位。 这些创新的动机是我们与国际流行病学数据库的合作， 在南部非洲（主要研究者Egger博士）和东部非洲（主要研究者Yiannoutsos博士）评价艾滋病（国际艾滋病和艾滋病研究所）， 东非社区卫生研究（ESTA）联盟（PI博士Havlir），一个随机分组 在所有的CD 4细胞计数中评估ART启动的社区范围内的益处的试验。开发的方法 将应用于这些数据源，以调查（i）监测抗逆转录病毒治疗（ART）的策略 和指导转换到二线方案，（二）直接和间接的影响，以社区为基础的艾滋病毒 预防战略和（iii）提供艾滋病毒护理的诊所为基础的方案的影响。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， 以明确和严格的方式使用方法。