Investigating the feasibility of idiographic network models.

Mansueto, Alessandra Chiara; Wiers, Reinout W.; Weert, Julia C.M. van; Schouten, Barbara C.; Epskamp, Sacha

doi:10.1037/met0000466

Cited by 56 publications

(65 citation statements)

References 81 publications

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“…The EBIC is controlled by setting the hyperparameter γ to an appropriate value between 0 and 1, where higher values imply a preference for parsimony . As in Mansueto et al (2022), γ was set to 0 to achieve a higher sensitivity. Because the lasso is sensitive to different variances, we z-transformed all variables before all analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Models that employ purely data-driven approaches based on data of a single individual can have several limitations, as they can ignore clinical judgment and can be hard to estimate and interpret properly (Burger et al, 2021;Klintwall et al, 2021). Several ideas might be able to counteract these issues by integrating clinical knowledge (Burger et al, 2021) or information from other individuals into individual networks (Beltz et al, 2016;Mansueto et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…To counteract this problem, we performed item-wise missing value imputation by applying the Kalman filter to every univariate time series of each individual. The Kalman filter has been shown to perform well in a previous simulation study of idiographic network analysis (Mansueto et al, 2022), albeit different methods for network estimation were used in that study. We used the Kalman filter from the R package imputeTS (Moritz & Bartz-Beielstein, 2017) in its default setting using a structural model that is fitted with maximum likelihood.…”

Section: Missing Datamentioning

confidence: 98%

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Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Siepe¹,

Sander²,

Schultze³

et al. 2022

Preprint

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Background: As phenotypes of depressive disorders (DD) are highly heterogenous, a growing number of studies investigate person-specific associations of depressive symptoms in time series data. Most available methods for estimating applicable models rely on the assumption that the associations between variables stay constant over time, which can be unrealistic in clinical contexts. To circumvent this limitation, we used a recently developed technique to estimate time-varying vector autoregressive models. Methods: In daily diary data of 20 participants with DD with a mean length of 274 days (SD = 82.4, range = 154-539), we modeled idiographic associations between core depressive symptoms, rumination, sleep, and quantity and quality of social contacts as idiographic time-varying dynamical networks. Results: Resulting models showed marked inter- as well as intraindividual differences. For some participants, associations between variables changed fast over time, whereas for others they showed more stability. Our results further indicated nonstationarity in all time series. Discussion: Idiographic symptom networks of depression can be of interest to clinicians and researchers as they can capture changes over time and provide detailed insights into the temporal course of mental disorders. Whilst the assumption of stationarity can hinder insights into important change processes, time-varying network models are a promising approach. We discuss limitations, their possible solutions, and recommendations for further use of the modeling technique.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Missing Datamentioning

confidence: 98%

See 1 more Smart Citation

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Siepe¹,

Sander²,

Schultze³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These methods are resource intensive. More perplexingly, methodological work shows that they need to be even more resource intensive to work properly (Mansueto et al, 2022). Even with extensive resources, these models are unlikely to be useful for scholars of belief systems because many of the attitudes and identities that make up political belief systems have high temporal stability (Brandt & Morgan, in press; Kiley & Vaisey, 2020).…”

Section: Existing Approaches For Measuring Within-person Psychologica...mentioning

confidence: 99%

Measuring the belief system of a person.

Brandt¹

2022

Journal of Personality and Social Psychology

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Theories of belief system structure and dynamics assume that belief systems are a person-level construct. However, measures of belief system structure do not measure the structure of person-level belief systems and instead measure aggregated belief system structure (e.g., the belief system in a particular country). In this paper, I show that a measure of conceptual similarity between attitudes and identities of a belief system works as a valid, reliable, flexible, and efficient measure of person-level belief system structure in the United States. In Studies 1 (N = 387), 2 (N = 389), and 3 (N = 598), I show conceptual similarity judgments are reliable and are related to measures of political engagement, political knowledge, attitude consistency, and preference congruence as predicted by computational models of belief system dynamics. In Studies 4 (N = 981) and 5 (N = 983), I show that conceptual similarity judgments are affected by partisan frames and that changes in conceptual similarity judgments are associated with attitude change as predicted by computational models of belief system dynamics. Conceptual similarity judgments can be used with a variety of attitudes and identities in easy to administer studies. It provides a tool to fill an empirical gap identified by theories of belief system dynamics.

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“…Further, while psychopathological processes are within-person, analyses operated at the group level, potentially limiting construct validity and further restricting direct clinical utility. 102 Use of, idiographic network analysis, 103 causal search algorithms, 104 and network intervention or outcome analytic approaches 11,79 could elucidate causal and intra-individual dynamics involved in the interplay between insomnia, depression, and anxiety symptoms. Such analyses could inform new directions in personalised, transdiagnostic treatments for patients experiencing these symptoms.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Insomnia, depression, and anxiety symptoms interact and individually impact functioning: A network and relative importance analysis in the context of insomnia

Bard¹,

O’Driscoll²,

Miller³

et al. 2022

Preprint

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Study Objectives. Insomnia, depression, and anxiety show high rates of comorbidity and functional impairment. Transdiagnostic symptom interactions may be implicated in this comorbidity. This network analysis sought to assess how symptoms of insomnia, depression, and anxiety may interact and individuallypredict impairment across several domains for individuals with insomnia.Methods. Baseline psychometric data from a randomised controlled trial were analysed (N = 1,711). A regularised partial correlation network was estimated from the symptom data. Centrality (symptom connectivity), community structure (symptom clustering), and bridging (inter-community connectivity) wereassessed. The replicability of the network model was assessed via confirmatory analyses in a holdout sample. Separately, Shapley values were estimated to determine the relative importance of each symptom in predicting functioning (i.e., psychological wellbeing, psychosocial functioning, and physical healthimpairment).Results. The most connected nodes were uncontrollable worrying; trouble relaxing; and depressed mood/hopelessness. Five communities were identified with trouble relaxing identified as the bridge symptom between communities. The model showed good fit in the holdout sample. Low energy and depressive affectsymptoms (feelings of failure/guilt; depressed mood/hopelessness; anhedonia) were key predictors in the relative importance analysis across multiple domains of impairment.Conclusion. Trouble relaxing may be of clinical and transdiagnostic significance in the context of insomnia. In terms of how symptoms relate to functioning, it was clear that, while low energy and feelings of failure/guilt were prominent predictors, a range of symptoms are associated with functional impairment.Consideration of both symptoms and functional impairment across domains may be useful in determining targets for treatment.

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Investigating the feasibility of idiographic network models.

Cited by 56 publications

References 81 publications

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Measuring the belief system of a person.

Insomnia, depression, and anxiety symptoms interact and individually impact functioning: A network and relative importance analysis in the context of insomnia

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