Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.

ABSTRACT:

Background

Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model.

Methods

We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we var

SUBMITTER: Sauser Zachrison K 

PROVIDER: S-EPMC5192582 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature