Value of Information and Cost-Effectiveness Analysis to Optimize Research and Policy Decisions for HIV Care in Resource Limited Settings

摘要 K01研究和培训基金旨在使用创新的信息价值(VOI)分析方法来 有效地为与病毒学管理相关的潜在临床研究研究的优先顺序和设计提供信息 肯尼亚成人(15岁及以上)基于多洛替格雷(DTG)的ART失败，也将有更广泛的 对资源受限设置(RLS)的影响。将通用DTG引入RLS提出了新的问题 关于对基于DTG方案失败的艾滋病毒携带者的最佳临床管理。 进行进一步的临床研究可以提供信息并改进艾滋病毒治疗指南；然而，研究资金 资源是有限的。因此，确定资金优先顺序的方法将S的潜力最大化，这一点很重要 研究对下游健康的益处。建议的K01将利用以个人为基础的艾滋病毒流行病学 杜阿尔特博士为肯尼亚成年人(15岁及以上)人口开发的随机模型。这个项目的 研究目标(r1-3)是：r1)根据现有数据，评估药物的潜在成本-效果 基于DTG的耐药性测试为病毒载量未受抑制的患者选择二线抗逆转录病毒疗法提供依据 R2)计算从进一步相关研究中预期的最大潜在投资回报 在基于DTG的ART上处理病毒学故障，并确定哪些研究具有最大潜力 健康效益；以及R3)计算目标2中确定的潜在关键临床研究的最佳样本量。 这项研究的研究目标将为杜阿尔特博士提供实现他的培训目标(T1-4)的机会， 包括：T1)发展模型参数化和校准的贝叶斯理论和方法方面的专业知识， 进行VOI分析和设计临床研究；T2)发展VOI分析方面的专业知识，包括 元模型开发的统计方法；t3)发展使用开源决策分析的专业知识 方法，包括数据可视化工具；以及t4)理解 设计临床研究，并与肯尼亚和其他RLS的利益相关者发展关系，以便建立 研究合作。这些研究和培训目标将为杜阿尔特博士提供宝贵的专业知识 适用于与艾滋病毒以及其他传染病有关的一系列重要卫生政策问题 挑战，从而为他过渡到独立做好准备。他将计划提交一份R01赠款，重点是 告知临床研究的优先顺序和设计，以改善儿科和孕产妇的艾滋病毒健康结果 在RLS中。这款R01将利用杜阿尔特博士通过提议的K01获得的专业知识，并将其与他的 目前正在开展由赠款资助的试点工作，为受艾滋病毒影响的儿科和产妇人群建立模拟模型。 杜阿尔特博士将在华盛顿大学完成他的K01职业发展培训，该校提供 为培训计算方法并将其应用于全球卫生问题提供了出色的环境，如 这所大学与肯尼亚的机构有着25年的合作历史。

ABSTRACT This K01 research and training grant aims to use innovative value of information (VOI) analysis methods to efficiently inform the prioritization and design of potential clinical research studies related to managing virologic failure on dolutegravir (DTG)-based ART for adults (15 years and older) in Kenya, which will also have broader implications for resource-limited settings (RLS). The introduction of generic DTG to RLS raises new questions about optimum clinical management for people living with HIV for whom DTG-based regimens are failing. Conducting furtherclinicalresearch can informand improve HIV treatment guidelines; however, research funding resources are limited. Thus, it is important that methods for setting funding prioritie s maximize the potential downstream health benefits of research. The proposed K01 will leverage an individual-based HIV epidemiologic stochastic model, developed by Dr. Duarte, for a Kenyan adult (15 years and older) population. This project’s research aims (R1-3) are to: R1) Given currently available data, evaluate the potential cost-effectiveness of drug resistance testing to inform choice of second-line ART for patients with unsuppressed viral load on DTG-based ART; R2) Calculate the maximum potential returns on investment to be expected from further research related to managing virologic failure on DTG-based ART and identify which research studies have the greatest potential health benefit; and R3) Calculate the optimal sample size for potential key clinical studies identified in Aim 2. The research aims of this study will provide Dr. Duarte the opportunity to pursue his training aims ( T1-4), including: T1) Develop expertise in Bayesian theory and methods for model parameterization and calibration, conducting VOI analyses, and designing clinical studies; T2) Develop expertise in VOI analysis, including statistical methods for meta-model development; T3) Develop expertise in using open-source decision analysis methods, including data visualization tools; and T4) Understand the methodologic and pragmatic aspects of clinical study design, and develop relationships with stakeholders in Kenya and other RLS, in order to build research collaborations. These research and training goals will provide Dr. Duarte with valuable expertise that is applicable to a wide range of important health policy issues relevant to HIV, as well as other infectious disease challenges, thereby preparing him to transition to independence. He will plan to submit an R01 grant focused on informing the prioritization and design of clinical research to improve pediatric and maternal HIV health outcomes in RLS. This R01 will leverage the expertise Dr. Duarte gains through the proposed K01 and combine it with his ongoing, pilot grant-funded work building simulation models for pediatric and maternal HIV-affected populations. Dr. Duarte will complete his K01 career development training at the University of Washington, which provides an outstanding environment for training in computational methods and applying them to global health issues, as the university has a strong >25-year history of collaboration with Kenyan institutions.