Estimating psychopathological networks: be careful what you wish for

TL;DR

This paper critically examines the assumptions underlying psychopathological network models, highlighting how imposed structural assumptions like sparsity can bias interpretations, especially given limited sample sizes in psychological research.

Contribution

It demonstrates through simulations that assumptions such as sparsity can distort the inferred network structure, emphasizing the need for cautious interpretation.

Findings

Assuming sparsity can lead to biased network estimates.

Simulation studies show the impact of structural assumptions.

Caution is needed when interpreting network models.

Abstract

Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature. These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are introduced to reduce the number of free parameters. Incorporating these assumptions, however, means that the resulting network will lead to reflect the particular structure assumed by the estimation method---a crucial and often ignored aspect of psychopathological networks. For example, observing a sparse structure and simultaneously assuming a sparse structure does not imply that the true model is, in fact, sparse. To illustrate this point, we discuss recent literature and show the effect of the assumption of sparsity in three simulation studies.