PDF(Pubmed)
Abstract：
BACKGROUND: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate.
OBJECTIVE: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.
METHODS: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation.
RESULTS: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation.
CONCLUSIONS: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model\'s effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.
OBJECTIVE: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.
METHODS: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation.
RESULTS: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation.
CONCLUSIONS: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model\'s effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.
摘要：
背景：人口规模，患病率,和发病率是影响公共卫生规划和政策的重要指标。然而,利益相关者经常负责设定绩效目标，报告全球指标，并根据这些变量的多个(通常不协调)估计来设计策略，他们经常在没有正式的，达成共识估计的透明框架。
目的：本研究旨在描述一个模型，以综合多个研究估计，同时结合利益相关者的知识，引入一个RShiny应用程序来实现该模型，并使用真实数据演示模型和应用程序。
方法：在本研究中，我们开发了贝叶斯分层模型来综合多个研究估计,使用户能够将每个估计的质量作为置信度评分.该模型被实现为用户友好的RShiny应用程序，旨在针对人口规模估计的从业者。在Stan中对基础贝叶斯模型进行了编程，以进行有效的采样和计算。
结果：使用基于生物行为调查的人口规模估计（以及伴随的信心得分）对撒哈拉以南非洲一个国家的3个调查地点的女性性工作者和与男性发生性关系的男性进行了演示。将包含置信度得分的共识结果与不存在的情况进行比较，根据应用程序提供的指标，对于下落不明的变化，具有置信度分数的结果显示表现更好。
结论：三角测量模型的实用性，包括合并信心得分，作为一个用户友好的应用程序使用用例示例演示。我们的结果为该模型在产生准确的共识估计方面的有效性提供了经验证据，并强调了可访问模型和应用程序对公共卫生的重大影响。它为综合多个估计的长期问题提供了解决方案，可能导致更知情和基于证据的决策过程。三角测量具有广泛的实用性和灵活性，可以在各种其他环境和地区进行调整和使用，以应对类似的挑战。
目的：本研究旨在描述一个模型，以综合多个研究估计，同时结合利益相关者的知识，引入一个RShiny应用程序来实现该模型，并使用真实数据演示模型和应用程序。
方法：在本研究中，我们开发了贝叶斯分层模型来综合多个研究估计,使用户能够将每个估计的质量作为置信度评分.该模型被实现为用户友好的RShiny应用程序，旨在针对人口规模估计的从业者。在Stan中对基础贝叶斯模型进行了编程，以进行有效的采样和计算。
结果：使用基于生物行为调查的人口规模估计（以及伴随的信心得分）对撒哈拉以南非洲一个国家的3个调查地点的女性性工作者和与男性发生性关系的男性进行了演示。将包含置信度得分的共识结果与不存在的情况进行比较，根据应用程序提供的指标，对于下落不明的变化，具有置信度分数的结果显示表现更好。
结论：三角测量模型的实用性，包括合并信心得分，作为一个用户友好的应用程序使用用例示例演示。我们的结果为该模型在产生准确的共识估计方面的有效性提供了经验证据，并强调了可访问模型和应用程序对公共卫生的重大影响。它为综合多个估计的长期问题提供了解决方案，可能导致更知情和基于证据的决策过程。三角测量具有广泛的实用性和灵活性，可以在各种其他环境和地区进行调整和使用，以应对类似的挑战。
