Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials.

ABSTRACT: Estimating parameters corresponding to mean outcomes and their intricate association structures in cluster randomized trials (CRTs) can pose significant methodological challenges. This paper introduces a novel framework that leverages network concepts to represent complex dependency structures and estimate these parameters using generalized estimating equations (GEE). We focus on modeling complex correlation structures by partitioning observations into potentially overlapping groups of interrelated data, where observations are assumed locally exchangeable within each group. This network GEE framework is inherently flexible, and we demonstrate its application to multiple exchangeable structures (simple, nested, block), moving average structures, and exponential decay structures. Furthermore

SUBMITTER: Chen T 

PROVIDER: S-EPMC12766921 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature