Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data

Abstract

We propose a novel marginal additive model (MAM) for modelling cluster‐correlated data with non‐linear 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 (i) a longitudinal study of beaver foraging behaviour, and (ii) a spatial analysis of Loaloa infection in West Africa.This article is protected by copyright. All rights reserved.