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

摘要 这项 K01 研究和培训补助金旨在利用创新的信息价值 (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顺序和设计 肯尼亚成人（15 岁及以上）基于多替拉韦 (DTG) 的 ART 治疗失败，这也将带来更广泛的影响 对资源有限环境（RLS）的影响。将通用 DTG 引入 RLS 引发了新问题 关于针对基于 DTG 的治疗方案失败的 HIV 感染者的最佳临床管理。 进行进一步的临床研究可以为艾滋病毒治疗指南提供信息并改进；然而，研究经费 资源是有限的。因此，确定资助优先顺序的方法必须最大限度地发挥潜力，这一点很重要。 研究的下游健康益处。拟议的 K01 将利用基于个人的 HIV 流行病学研究 随机模型，由 Duarte 博士针对肯尼亚成年人（15 岁及以上）人群开发。这个项目的 研究目标 (R1-3) 是： R1) 根据当前可用数据，评估药物的潜在成本效益 耐药性测试可为基于 DTG 的病毒载量未抑制的患者选择二线 ART 提供信息 艺术； R2) 计算通过进一步的相关研究预计的最大潜在投资回报 管理基于 DTG 的 ART 的病毒学失败，并确定哪些研究具有最大潜力 健康益处； R3) 计算目标 2 中确定的潜在关键临床研究的最佳样本量。 这项研究的研究目的将为杜阿尔特博士提供实现他的培训目标的机会（T1-4）， 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 分析并设计临床研究； T2) 培养 VOI 分析方面的专业知识，包括 元模型开发的统计方法； T3) 培养使用开源决策分析的专业知识 方法，包括数据可视化工具； T4) 了解方法论和实用性方面 临床研究设计，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相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 R01 拨款，重点关注 为临床研究的优先顺序和设计提供信息，以改善儿科和孕产妇艾滋病毒健康结果 在 RLS 中。该 R01 将利用 Duarte 博士通过拟议的 K01 获得的专业知识，并将其与其 正在进行的、由赠款资助的试点工作，为受艾滋病毒影响的儿科和孕产妇群体建立模拟模型。 杜阿尔特博士将在华盛顿大学完成他的 K01 职业发展培训，该培训提供 为计算方法培训并将其应用于全球健康问题提供了一个出色的环境，例如 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.