Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size.

ABSTRACT:

Background

Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively.

Methods

Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed 'exposure'), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. Ho

SUBMITTER: Pavlou M 

PROVIDER: S-EPMC8254921 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature