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

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

Modelling the Longitudinal Dynamics of Paranoia in Psychosis:A Temporal Network Analysis Over 20 Years

Paranoia is a highly debilitating, core element of psychosis, although is poorly managed. Theories of paranoia mostly interface with short-scale or cross-sectional data models, leaving the longitudinal course of paranoia underspecified. Here, we develop an empirical characterisation of two aspects of paranoia - persecutory and referential delusions - in individuals with psychosis over 20 years. We examine delusional dynamics by applying a Graphical Vector Autoregression Model to data collected from the Chicago Follow-up Study. We adjusted for age, sex, IQ, and antipsychotic use. We found that referential and persecutory delusions are central themes, supported by other primary delusions, and are strongly autoregressive. In a second analysis we demonstrate that social factors influence the severity of referential, but not persecutory, delusions. We suggest that persecutory delusions represent central, resistant states in the cognitive landscape, whereas referential beliefs are more flexible, offering an important window of opportunity for intervention. Our data models can be collated with prior biological, computational, and social work to contribute toward a more complete theory of paranoia and provide more time-dependent evidence for optimal treatment targets