Network Estimation from Time Series and Panel Data

Burger, Julian; Hoekstra, Ria H. A.; Mansueto, Alessandra C.; Epskamp, Sacha

doi:10.4324/9781003111238-13

Cited by 14 publications

(27 citation statements)

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“…These patterns raise questions about the temporal scale at which change takes place. Future studies might utilize recent methodological developments in the field of network modeling (Burger et al, 2022 ) to disentangle the relationship between mindfulness and affect at the within-lagged, within-contemporaneous, and between-subjects levels to more clearly separate these levels.…”

Section: Discussionmentioning

confidence: 99%

The Relationship Between Negative Affect, State Mindfulness, and the Role of Personality

Karl

Fischer

2022

Mindfulness

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Objectives Are affective states influencing state mindfulness and can this explain the link between personality and mindfulness? Mindfulness is commonly thought to decrease negative affect, but a number of studies have reported reductions in mindfulness in negative affect situations. This highlights a potential mechanism explaining previously observed negative relationships between individual differences such as Neuroticism and mindfulness, via their shared relationship with negative affect. Methods In an experiment, 331 participants were exposed to a negative affect stimulus to investigate whether previously established relationships between Neuroticism, Behavioral Inhibition, and Mindfulness are due to differences in negative affect reactivity. It was expected that participants high on Neuroticism and Behavioral Inhibition to show greater negative affect reactivity which in turn would reduce their state Non-Judgmental Acceptance and Acting with Awareness. Results While change in negative affect was related to lower Non-Judgmental Acceptance and Acting with Awareness, negative affect reactivity did not mediate the relationship between Neuroticism/BIS and these state mindfulness facets. Importantly, only Non-Judgmental Acceptance and Acting with Awareness facets of mindfulness were affected by negative affect change, but not Attention. This indicates that greater negative affect might require allocation of cognitive resources to stress-related processes such as threat monitoring, reducing mindful emotion processing but not attention. Conclusions The current study found that situational mindfulness might be influenced by state affect highlighting the need to more strongly consider the environmental factors which can shape affect in mindfulness interventions. Trial Registration Open Science Framework ( https://osf.io/kmyh2 ).

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Section: Discussionmentioning

confidence: 99%

The Relationship Between Negative Affect, State Mindfulness, and the Role of Personality

Karl

Fischer

2022

Mindfulness

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“…Future research should aim to investigate which statistical models are best suited for this approach, work towards providing concrete practical recommendations for implementation, and test if resulting networks can indeed improve therapy outcomes as evaluated by clinicians and patients. (Burger, Hoekstra, et al, 2022). Temporal and contemporaneous relationships can be calculated via components of the VAR model, which predicts the current score of a variable from previous scores of all variables.…”

Section: Discussionmentioning

confidence: 99%

“…For example, stationarity can be investigated by visualizing the time series, by performing formal tests such as the Dickey-Fuller test (Dickey & Fuller, 1979), and by employing change point detection algorithms (Aminikhanghahi & Cook, 2017). Although specific deviations from assumptions can be accounted for by transformations (e.g., removing time-related trends; Burger, Hoekstra, Mansueto, & Epskamp, 2022), some research questions explicitly aim at understanding mechanisms related to change, for example modeling the effects of interventions. In such cases, it may be possible to use the VAR model, including the priors discussed in this paper, for data collected within stationary time-periods (e.g., prior to the start of an intervention), however, the VAR model does not allow one to model shifts between disorder states typically following interventions (Henry et al, 2020).…”

Section: Capturing Relevant Clinical Phenomenamentioning

confidence: 99%

A clinical PREMISE for personalized models: Towards a formal integration of case formulations and statistical networks

Burger¹,

Epskamp²,

Veen³

et al. 2021

Preprint

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Statistical innovations allow clinicians to estimate personalized networks from longitudinal data, for example data collected via the Experience Sampling Method (ESM). Such networks can generate insights that may be relevant for constructing case formulations, and therefore guide the selection of personalized treatment targets. While the notion of personalized networks aligns well with the way clinicians think and reason, there are currently several barriers to clinical implementation that limit the utility of such models. First, the most popular network estimation routines are data-driven and do not allow clinicians to incorporate their expertise and theory. Second, network models have many parameters, which can make accurate estimation challenging. Finally, network estimation requires technical skills that are not regularly taught in clinical programs. In this article, we introduce PREMISE, an approach that formally integrates case formulations with personalized network estimation. Using prior elicitation techniques, clinical working hypotheses are translated into formal models, which can subsequently inform network estimation from ESM data using Bayesian inference. PREMISE tackles the three challenges described above: Incorporating clinical information into network estimation systematically allows theoretical and data-driven integration, which in turn increases the accuracy of network estimation techniques. In addition, we implemented the principles of PREMISE into a practical web-based toolkit that generates intuitive feedback, thereby facilitating clinical implementation. To illustrate its clinical potential, we use PREMISE to estimate clinically informed networks for a client suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.

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“…In this example, the clinical prior was derived from a verbal patient report, and the models have been estimated with different amounts of available data (i.e., after 2 weeks and 4 weeks), mimicking the updating of personalized networks during biweekly therapy sessions. Another example on eating disorders has recently been published elsewhere (Burger et al, 2022).…”

Section: Clinical Example: Patient With Obsessive-compulsive Disordermentioning

confidence: 99%

A clinical PREMISE for personalized models: Toward a formal integration of case formulations and statistical networks.

Burger¹,

Epskamp²,

Veen³

et al. 2022

Journal of Psychopathology and Clinical Science

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Over the past decade, the idiographic approach has received significant attention in clinical psychology, incentivizing the development of novel approaches to estimate statistical models, such as personalized networks. Although the notion of such networks aligns well with the way clinicians think and reason, there are currently several barriers to implementation that limit their clinical utility. To address these issues, we introduce the Prior Elicitation Module for Idiographic System Estimation (PREMISE), a novel approach that formally integrates case formulations with personalized network estimation via prior elicitation and Bayesian inference. PREMISE tackles current implementation barriers of personalized networks; incorporating clinical information into personalized network estimation systematically allows theoretical and data-driven integration, supporting clinician and patient collaboration when building a dynamic understanding of the patient’s psychopathology. To illustrate its potential, we estimate clinically informed networks for a patient suffering from obsessive–compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.

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Network Estimation from Time Series and Panel Data

Cited by 14 publications

References 0 publications

The Relationship Between Negative Affect, State Mindfulness, and the Role of Personality

The Relationship Between Negative Affect, State Mindfulness, and the Role of Personality

A clinical PREMISE for personalized models: Towards a formal integration of case formulations and statistical networks

A clinical PREMISE for personalized models: Toward a formal integration of case formulations and statistical networks.

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