Ott Miles Q, Hogan Joseph W, Gile Krista J, Linkletter Crystal, Barnett Nancy P
Augsburg College, Minneapolis, U.S.A.
Brown University, Providence, U.S.A.
Stat Med. 2016 Aug 30;35(19):3303-18. doi: 10.1002/sim.6925. Epub 2016 Mar 4.
Peers are often able to provide important additional information to supplement self-reported behavioral measures. The study motivating this work collected data on alcohol in a social network formed by college students living in a freshman dormitory. By using two imperfect sources of information (self-reported and peer-reported alcohol consumption), rather than solely self-reports or peer-reports, we are able to gain insight into alcohol consumption on both the population and the individual level, as well as information on the discrepancy of individual peer-reports. We develop a novel Bayesian comparative calibration model for continuous, count, and binary outcomes that uses covariate information to characterize the joint distribution of both self and peer-reports on the network for estimating peer-reporting discrepancies in network surveys, and apply this to the data for fully Bayesian inference. We use this model to understand the effects of covariates on both drinking behavior and peer-reporting discrepancies. Copyright © 2016 John Wiley & Sons, Ltd.
同龄人往往能够提供重要的额外信息，以补充自我报告的行为测量数据。推动这项工作的研究收集了居住在新生宿舍的大学生所形成的社交网络中有关酒精的数据。通过使用两种不完美的信息来源（自我报告和同龄人报告的酒精消费量），而不是仅使用自我报告或同龄人报告，我们能够深入了解总体和个体层面的酒精消费情况，以及有关个体同龄人报告差异的信息。我们开发了一种新颖的贝叶斯比较校准模型，用于连续、计数和二元结果，该模型使用协变量信息来刻画网络中自我报告和同龄人报告的联合分布，以估计网络调查中的同龄人报告差异，并将其应用于数据进行全贝叶斯推断。我们使用这个模型来理解协变量对饮酒行为和同龄人报告差异的影响。版权所有© 2016约翰·威利父子有限公司。
