The Sum of All Fears: Comparing Networks Based on Symptom Sum-Scores.

Abstract

Researchers are often interested in comparing statistical network models estimated across groups that are defined by the sum-score of the modeled variables. A prominent example is an analysis that compares networks of individuals with and without a diagnosis of a certain disorder. Recently, several authors suggested that this practice may lead to invalid inferences by introducing Berkson's bias. In this paper, we show that whether bias is present or not depends on which research question one aims to answer. We review five possible research questions one may have in mind when they separately estimate network models in groups that are based on sum-scores. For each research question we provide an illustration with a bivariate example and discuss the exact nature of the bias, if present. We show that if one is indeed interested in the network models of the groups defined by the sum-score, no bias is introduced. However, if one is interested in the network model in the general population, differences across groups defined by a variable other than the sum-score, detecting population heterogeneity, or inferring direct causal relations, then bias will be introduced in most situations. Finally, we discuss for each research question how bias can be avoided.