Statistical Methods for Evaluating and Guiding Implementation of New HIV Prevention Strategies

评估和指导新艾滋病预防策略实施的统计方法

• 批准号: 10593374
• 负责人: Holly Janes
• 金额: $ 4.11万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593374
• 关键词: AIDS prevention; AIDS/HIV problem; Address; Adherence; Anti-Retroviral Agents; Behavioral; Clinical; Clinical Trials Design; Collaborations; Complex; Crossover Design; Data; Data Set; Development; Economic Factors; Effectiveness; Ensure; Ethics; Goals; HIV; HIV Infections; HIV prevention trials network; HIV risk; Incidence; Individual; Infusion procedures; Injectable; Intervention; Laboratories; Lead; Logistics; Methods; Monoclonal Antibodies; Observational Study; Oral; Participant; Performance; Pharmaceutical Preparations; Phase; Placebo Control; Placebos; Population; Positioning Attribute; Prevention; Prevention strategy; Public Health Practice; Randomized; Research Design; Research Personnel; Risk; Science; Statistical Data Interpretation; Statistical Methods; Structure; Translating; Vaccines; Work; analytical method; arm; base; combat; cost; design; effective intervention; efficacy testing; efficacy trial; fight against; global health; implementation evaluation; implementation intervention; implementation questions; implementation strategy; improved; member; method development; multiple data sources; next generation; novel; pandemic disease; pre-exposure prophylaxis; preventive intervention; research study; simulation; social; statistical learning; tool; treatment as prevention; trial design; uptake; vaccine trial

PROJECT SUMMARY The HIV prevention field has advanced dramatically in recent years through the development of antiretroviral- based prevention strategies such as Treatment-as-Prevention and oral pre-exposure prophylaxis (PrEP). Numer- ous challenges remain that impede the implementation of these interventions and HIV remains a major global health problem. We need new preventive interventions to combat the pandemic. The rapid advancements in the field have complicated the statistical design of efficacy trials of new interventions. For instance, oral PrEP is now part of the standard HIV prevention package offered to trial participants, which poses challenges in ensur- ing adequate statistical power. Currently, available statistical methods for guiding the implementation of effective interventions are inadequate. Considering this complex HIV prevention context, our goal in Aim 1 is to iden- tify suitable efficacy trial designs for evaluating the next generation of HIV prevention tools. We will consider crossover designs, sequential randomization designs, active-arm only designs, and non-inferiority designs with adaptive margins; each of these approaches addresses a key limitation of the prototypical phase 2b/3 trial de- sign currently in use. Through simulation studies and application to candidate interventions, we will investigate the relative statistical performance of the designs. We will also work with leaders in the HIV prevention field, to identify the clinical, ethical, logistical issues and other critical factors that must be considered. We will use these factors to refine our design comparisons and to identify the trial design that is most appropriate for each setting. Under Aim 2, we will address specific implementation questions in HIV prevention, through the development of improved statistical analysis methods. We will develop methods for evaluating sub-population-specific HIV risk and prevention efficacy, to identify strategies for implementing interventions, and for bridging HIV incidence and prevention efficacy to new settings or populations. Our approach will rely on fewer assumptions than existing methods, use a framework that leverages statistical learning to extract information from multiple predictors of risk and efficacy, and accommodate data from multiple sources. We will use important and relevant datasets in HIV prevention and simulation studies designed to mimic their structure and content to gauge the performance of the approaches. Our team members include lead statistical investigators in the major HIV prevention trial networks, with considerable expertise in statistical methods development and clinical trial design, and established collabo- rations with other leaders in the clinical and laboratory science of HIV prevention. Our positions and connections make us uniquely poised to translate the novel methods into practice.

项目摘要 近年来，通过开发抗逆转录病毒药物，艾滋病毒预防领域取得了巨大进展， 基于预防策略，如治疗即预防和口服暴露前预防（PrEP）。数字- 然而，阻碍这些干预措施实施的各种挑战依然存在，艾滋病毒仍然是全球的一个主要问题， 健康问题。我们需要采取新的预防性干预措施来防治这一流行病。快速发展， 这些领域使新干预措施的有效性试验的统计设计复杂化。例如，口服PrEP是 现在是向试验参与者提供的标准艾滋病毒预防包的一部分，这对确保 足够的统计能力。目前，可用统计方法指导实施有效的 干预措施不足。考虑到这一复杂的艾滋病毒预防背景，我们的目标1是确定- 确定合适的有效性试验设计，以评估下一代艾滋病毒预防工具。我们会考虑 交叉设计、序贯随机化设计、仅活性组设计和非劣效性设计， 自适应边界;这些方法中的每一种都解决了原型2b/3期试验的关键限制， 目前正在使用的标志。通过模拟研究和候选干预措施的应用，我们将调查 设计的相对统计性能。我们还将与艾滋病毒预防领域的领导人合作， 确定临床、伦理、后勤问题和其他必须考虑的关键因素。我们将会用这些 因素，以完善我们的设计比较，并确定最适合每种环境的试验设计。 在目标2下，我们将通过制定 改进统计分析方法。我们将开发评估亚人群特定艾滋病毒风险的方法 和预防效率，确定实施干预措施的战略，并减少艾滋病毒的发病率， 对新环境或人群的预防效果。我们的方法将依赖于比现有更少的假设 方法，使用一个框架，利用统计学习从多个风险预测因子中提取信息 和效率，并容纳来自多个来源的数据。我们将使用重要和相关的数据集在艾滋病毒 预防和模拟研究，旨在模拟其结构和内容，以衡量 接近。我们的团队成员包括主要艾滋病毒预防试验网络的主要统计调查员， 在统计方法开发和临床试验设计方面具有相当的专业知识，并建立了合作关系， 与艾滋病毒预防的临床和实验室科学的其他领导人一起进行定量。我们的职位和关系 使我们能够将新方法转化为实践。