TY - JOUR
T1 - ConNEcT
T2 - A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time
AU - Bodner, Nadja
AU - Bringmann, Laura
AU - Tuerlinckx, Francis
AU - de Jonge, Peter
AU - Ceulemans, Eva
N1 - Funding Information:
The research leading to the results reported in this paper was sponsored in part by research grants from the Fund for Scientific Research-Flanders (FWO, Project No. G066316N awarded to Eva Ceulemans and Francis Tuerlinckx) and by the Research Council of KU Leuven (C14/19/054 and PDM/20/062). The raw data analyzed during the current study are available from the corresponding author on reasonable request. HICLAS software can be requested by emailing eva.ceulemans@kuleuven.be.. The beta version of the R package ConNEcT (0.6.3) can be downloaded from OSF at https://osf.io/rfxhc/ .
Publisher Copyright:
© 2021, The Psychometric Society.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2022/3
Y1 - 2022/3
N2 - Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.
AB - Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.
KW - binary data
KW - depression
KW - individual differences
KW - network analysis
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85107594912&partnerID=8YFLogxK
U2 - 10.1007/s11336-021-09765-2
DO - 10.1007/s11336-021-09765-2
M3 - Article
AN - SCOPUS:85107594912
VL - 87
SP - 107
EP - 132
JO - Psychometrika
JF - Psychometrika
SN - 0033-3123
IS - 1
ER -