Developing causal inference methods to evaluate and leverage spillover effects through social Interactions for designing improved HIV prevention interventions

项目总结/摘要 本提案的总体目标是开发创新的统计方法， 艾滋病毒治疗和预防干预措施，沿着更有效的实施战略。 由于艾滋病的二次传播和预防行为的社会影响， 一个人可以对艾滋病毒感染的风险、风险行为、护理保留和 其他个体的治疗依从性。这种机制，在因果推理中被称为“干扰”，仍然是一种 这是艾滋病毒干预措施评价的主要挑战。 考虑干扰的统计方法对于有效估计个体效应是必要的。 干预措施和总体人口影响，以及了解社会背景 通过溢出效应以及如何利用它发挥作用。该提案将开发创新方法， 1)在整群随机抽样中，理清随时间变化的一揽子干预措施的个体和溢出效应， 干扰和不遵守指定组件的化试验; 2）基于网络和集群的 随机研究，纠正由于干扰集（即个体集）的错误指定而导致的偏倚 他们的治疗会影响其他人的结果3)找出更有可能影响同龄人的人 采取行为改变，并评估针对这些人的策略的有效性。 这个项目将为感兴趣的因果问题定义新的因果被估量，并扩展边际结构 建模方法，以调整混杂和溢出，并评估假设的战略杠杆作用 成分特异性效应和影响异质性。传输和社会测量误差的偏差校正 影响网络将基于主要研究/验证研究方法，比较系统发育的使用， 基于集群、社会和性网络以及基于空间的网络和集群。用户友好的软件 将制定并公布拟议方法的实施办法，以促进其吸收。到 为了进一步促进传播，将提供关于新方法和软件的短期课程。 统计方法的发展将受到激励，并应用于两个大型集群随机试验， 博茨瓦纳（BCPP）和南非（TasP）以及三项基于网络的同伴教育研究（HPTN 037，CHAT， STEP），为个人和社区艾滋病毒干预措施的有效组合提供了新的见解 在这方面，我们需要在各级采取行动，并将其纳入新的战略，以利用溢出效应，加强这些干预措施的影响。的方法 将广泛适用于艾滋病毒和其他传染病的许多公共卫生干预措施。 我们的提案符合国家心理健康研究所艾滋病研究部的使命，因为 以及最近的NOSI（NOT-AI-21-054），以推进行为和生物医学的开发和测试， 通过结合社会背景和行为科学来理解和利用传播， 网络和社会影响，以及它们的社会决定因素。

Project Summary/Abstract The overarching goal of this proposal is to develop innovative statistical methods for designing more effective HIV treatment and prevention interventions, along with more effective implementation strategies to deliver them. Due to HIV secondary transmission and social influence of preventive behaviors, the intervention received by one individual can have an effect (or spill over) on the risk for HIV infection, risk behaviors, retention in care and treatment adherence of other individuals. This mechanism, called ‘interference’ in causal inference, remains a major challenge for the evaluation of HIV interventions. Statistical methods accounting for interference are necessary for valid estimation of the individual effect of an intervention and of the overall population effect, as well as for understanding the extent to which social context plays a role though spillover effects and how it can be leveraged. This proposal will develop innovative methods to 1) disentangle individual and spillover effects of time-varying package intervention components in cluster random- ized trials with interference and non-compliance to the assigned components; 2) in network-based and cluster randomized studies, correct for bias due to misspecification of the interference sets, that is, the sets of individuals whose treatment affects the outcome of others. 3) identify individuals who are more likely to influence their peers to adopt behavioral changes and evaluate the improved effectiveness of strategies that target these individuals. This project will define novel causal estimands for the causal questions of interest, and extend marginal structural modeling methodology to adjust for confounding and spillover and to evaluate hypothesized strategies leveraging component-specific effects and influence heterogeneity. Bias correction for mismeasured transmission and social influence networks will be based on a main study/validation study approach comparing the use of phylogenetic- based clusters, social and sexual networks, and spatially-based networks and clusters. User-friendly software implementing the proposed methods will be developed and made publicly available to facilitate their uptake. To further facilitate dissemination, short courses about the new methods and software will be offered. The development of statistical methods will be motivated and applied to two large cluster randomized trials in Botswana (BCPP) and South Africa (TasP) and three network-based peer education studies (HPTN 037, CHAT, STEP), providing new insights into effective combinations of HIV interventions at the individual and community level and into novel strategies to leverage spillover and strengthen the impact of these interventions. The methods will be broadly applicable to many public health interventions for HIV and other infectious diseases. Our proposal fits within the mission of the National Institute of Mental Health - Division of AIDS Research, as well as that of a recent NOSI (NOT-AI-21-054), to advance the development and testing of behavioral and biomed- ical interventions by incorporating social context and behavioral science to understand and leverage transmission networks and social influence, as well as their social determinants.