This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modelled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding direct and indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally-reduced modification of the intrinsic conditional autoregression (ICAR) model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela. This article is protected by copyright. All rights reserved.

中文翻译：

---

具有计数结果的集群随机试验中的空间回归和溢出效应

本文描述了分析来自具有计数结果的集群随机试验数据的方法，同时考虑了间接效应和空间效应。使用干预臂内深度测量的新应用来模拟间接效应。即使错误指定了建议的模型，也可以准确估计直接和间接影响。我们使用具有高斯随机效应的空间回归模型，其中单个结果的分布相对于泊松过度分散，相应的直接和间接效应具有边际解释。为了避免空间混淆，我们使用正交回归，其中随机效应使用内在条件自回归 (ICAR) 模型的同方差和降维修改来表示空间依赖性。我们使用来自委内瑞拉特鲁希略的针对登革热蚊媒埃及伊蚊的配对集群随机试验的空间数据来说明该方法。本文受版权保护。版权所有。