Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data
提出一种新的边际可加模型，用于分析聚类相关数据中的非线性总体平均关联，同时估计聚类间变异和进行聚类特定预测，适用于纵向研究和空间分析。
Abstract We propose a novel marginal additive model (MAM) for modeling cluster‐correlated data with nonlinear population‐averaged associations. The proposed MAM is a unified framework for estimation and uncertainty quantification of a marginal mean model, combined with inference for between‐cluster variability and cluster‐specific prediction. We propose a fitting algorithm that enables efficient computation of standard errors and corrects for estimation of penalty terms. We demonstrate the proposed methods in simulations and in application to (a) a longitudinal study of beaver foraging behavior and (b) a spatial analysis of Loa loa infection in West Africa.