Improvements to the network scale-up method for studying hard-to-reach population

描述(由申请人提供)：估计难以接触到的人口的规模对于公共卫生和公共政策中的许多问题很重要。人口规模估计在艾滋病毒/艾滋病研究中尤其紧迫，因为对最危险的人群-注射毒品者、女性性工作者和男男性行为者-的人口规模的可靠估计对于了解和控制该流行病的传播至关重要。不幸的是，目前的统计方法无法应对这一挑战。缺乏关于这些高危人群规模的及时和准确的信息，是设计和评估艾滋病毒预防方案的关键障碍。这项研究的目标是改进网络扩大法，这是一种很有前途的统计方法，用于估计难以到达的群体的规模。网络规模估计来自从一般人群中随机抽样的个人网络收集的调查数据，在估计难以到达的群体的大小方面与其他方法相比具有重要优势：1)它可以很容易地跨国家和时间标准化，因为它需要一般人口的随机样本，这可能是世界上使用最广泛的抽样设计；2)它可以在相同的数据收集中产生许多目标人口的大小的估计，而许多替代方法需要对每个感兴趣的人口进行不同的数据收集；以及3)它可以部分自我验证，因为它很容易应用于已知大小的人口。然而，尽管有这些吸引人的特点，世界各地的研究人员和政府越来越多地使用这种方法，但人们对扩大方法的统计基础知之甚少，关键的实施问题仍然没有得到回答。这项研究将通过数学建模、计算机模拟和对现有扩大数据集的分析来实现，将使研究人员能够收集关于难以接触到的群体的更准确和更有用的信息。此外，实现这些目标所需的统计发展将丰富我们从抽样数据中了解完整网络的一般能力。因此，该项目将有关网络抽样的基础研究与对遏制艾滋病毒/艾滋病流行的全球努力的重要贡献结合在一起。

DESCRIPTION (provided by applicant): Estimating the sizes of hard-to-reach populations is important for many problems in public health and public policy. Population size estimation is particularly pressing in HIV/AIDS research because reliable estimates of the sizes of the most at-risk populations---drug injectors, female sex workers, and men who have sex with men---are critical for understanding and controlling the spread of the epidemic. Unfortunately, current statistical methods are not up to this challenge. The lack of timely and accurate information about the sizes of these most at-risk groups is a critical barrier to the design and evaluation of HIV prevention programs. The goal of this research is to improve the network scale-up method, a promising statistical approach for estimating the sizes of hard-to-reach groups. Network scale-up estimates come from survey data collected about the personal networks of a random sample of the general population, and offers important advantages over other approaches for estimating the sizes of hard-to-reach groups: 1) it can easily be standardized across countries and time because it requires a random sample of the general population, perhaps the most widely used sampling design in the world; 2) it can produce estimates of the sizes of many target populations in the same data collection, whereas many alternative methods require distinct data collections for each population of interest; and 3) it can be partially self-validating because it an easily be applied to populations of known size. However, despite these appeal characteristics and growing use by researchers and governments around the world, the statistical foundations of the scale-up method are poorly understood and key implementation questions remain unanswered. This research, which will be achieved through a combination of mathematical modeling, computer simulation, and the analysis of existing scale-up data sets, will enable researchers to collect more accurate and more useful information about hard-to-reach groups. Further, the statistical developments needed to achieve these aims will enrich our general ability to learn about complete networks from sampled data. Thus, this project combines foundational research about sampling in networks with important contributions to the global effort to contain the HIV/AIDS epidemic.