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

摘要 该K 01研究和培训补助金旨在使用创新的信息价值（VOI）分析方法， 有效地告知与管理病毒学相关的潜在临床研究的优先级和设计 在肯尼亚，基于dolutegravir（DTG）的ART治疗成人（15岁及以上）失败，这也将有更广泛的 对资源有限环境（RLS）的影响。将通用DTG引入RLS提出了新的问题 关于DTG治疗方案失败的艾滋病毒感染者的最佳临床管理。 开展进一步的临床研究可以提供信息并改善艾滋病毒治疗指南;然而，研究资金 资源是有限的。因此，重要的是，确定资金优先次序的方法最大限度地发挥潜力， 研究的下游健康效益。拟议的K 01将利用基于个人的艾滋病毒流行病学 随机模型，由杜阿尔特博士，肯尼亚成年人（15岁及以上）的人口。此项目的 研究目的（R1-3）是：R1）根据现有数据，评估药物的潜在成本效益 耐药性检测，为基于DTG的病毒载量未受抑制的患者选择二线ART提供信息 ART; R2）计算从相关的进一步研究中预期的最大潜在投资回报 管理基于DTG的ART的病毒学失败，并确定哪些研究最有潜力 R3）计算目标2中确定的潜在关键临床研究的最佳样本量。 本研究的研究目的将为Duarte博士提供实现其培训目标（T1-4）的机会， 包括：T1）发展贝叶斯理论和模型参数化和校准方法的专门知识， 进行VOI分析，并设计临床研究; T2）发展VOI分析的专业知识，包括 元模型开发的统计方法; T3）开发使用开源决策分析的专业知识 方法，包括数据可视化工具;和T4）了解方法和实用方面的 临床研究设计，并与肯尼亚和其他RLS的利益相关者建立关系， 研究合作。这些研究和培训目标将为Duarte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R 01赠款，重点是 为临床研究的优先次序和设计提供信息，以改善儿科和孕产妇艾滋病毒健康结果 在RLS。该R 01将利用Duarte博士通过拟议的K 01获得的专业知识，并将其与他的联合收割机相结合。 正在进行的赠款资助的试点工作，为受艾滋病毒影响的儿童和孕产妇建立模拟模型。 博士杜阿尔特将在华盛顿大学完成他的K 01职业发展培训， 一个出色的环境，培训计算方法，并将其应用于全球健康问题， 该大学与肯尼亚机构有着超过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.