Anaya-Izquierdo Karim, Alexander Neal
Department of Mathematical Sciences, University of Bath, Bath, UK.
MRC Tropical Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Biometrics. 2021 Jun;77(2):490-505. doi: 10.1111/biom.13316. Epub 2020 Jul 2.
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 modeled 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 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.
本文描述了用于分析具有计数结果的整群随机试验数据的方法，该方法考虑了间接效应以及空间效应。间接效应通过在干预组内对深度度量的一种新颖应用来建模。即使所提出的模型设定错误，直接效应和间接效应也都能够被准确估计。我们使用具有高斯随机效应的空间回归模型，其中个体结果相对于泊松分布具有过度分散的分布，并且相应的直接效应和间接效应具有边际解释。为了避免空间混杂，我们使用正交回归，其中随机效应使用内在条件自回归模型的同方差且降维的修正来表示空间依赖性。我们使用来自委内瑞拉特鲁希略针对登革热蚊媒埃及伊蚊的配对整群随机试验的空间数据来说明该方法。
