Chen Guanqing, O'Malley A James
Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Biom J. 2025 Feb;67(1):e70030. doi: 10.1002/bimj.70030.
Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ) designed to enhance model computation and compare results to those under the uniform prior for . We use simulation to assess the performance of Bayesian point and interval estimators for each of the four models when the model that generated the data is used for estimation (precision assessment) and when each of the other three models instead generated the data (robustness assessment). We construct a United States New England region patient-sharing hospital network and apply the four network autocorrelation models to study the adoption of robotic surgery, a new medical technology, among hospitals using a cohort of United States Medicare beneficiaries in 2016 and 2017. Finally, we develop a deviance information criterion for each of the four models to compare their fit to the observed data and use posterior predictive p-values to assess the models' ability to recover specified features of the data. The results find that although the indirect peer effect of the propensity of peer hospital adoption on that of the focal hospital is positive under both latent response autocorrelation models, the direct peer effect of the peer hospital's probability of adopting robotic surgery on the probability of the focal hospital adopting robotic surgery decreases under both mean autocorrelation data models. However, neither of these associations is statistically significant.
尽管网络自相关模型在社会网络分析中得到了广泛应用，但针对二元因变量的网络自相关模型却出人意料地受到极少关注。在本文中，我们针对一个二元随机变量开发了四种网络自相关模型，该二元随机变量由以下情况定义：同伴效应（也称为社会影响或传染）是否作用于潜在连续结果，从而在正态或逻辑分布下产生间接效应；或者是否作用于观测结果本身的概率，在概率单位或逻辑链接函数下定义直接效应，以考虑结果之间的相互依赖性。对于所有模型，我们在转换后的同伴效应参数（ ）的均匀先验下采用贝叶斯方法进行模型估计，旨在增强模型计算，并将结果与 的均匀先验下的结果进行比较。我们使用模拟来评估当用于生成数据的模型用于估计时（精度评估）以及当其他三个模型中的每一个生成数据时（稳健性评估），这四种模型中每种模型的贝叶斯点估计和区间估计的性能。我们构建了美国新英格兰地区患者共享医院网络，并应用这四种网络自相关模型来研究2016年和2017年使用一组美国医疗保险受益人的医院中新型医疗技术机器人手术的采用情况。最后，我们为这四种模型中的每一个开发了偏差信息准则，以比较它们对观测数据的拟合情况，并使用后验预测p值来评估模型恢复数据指定特征的能力。结果发现，尽管在潜在响应自相关模型下，同伴医院采用倾向对焦点医院采用倾向的间接同伴效应是正向的，但在均值自相关数据模型下，同伴医院采用机器人手术的概率对焦点医院采用机器人手术概率的直接同伴效应均有所下降。然而，这些关联均无统计学意义。
