On the Use of Mixed Markov Models for Intensive Longitudinal Data

Haan-Rietdijk, Silvia de; Kuppens, Peter; Bergeman, C. S.; Sheeber, Lisa; Allen, Nicholas; Hamaker, Ellen L.

doi:10.1080/00273171.2017.1370364

Cited by 37 publications

(23 citation statements)

References 73 publications

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“…Applications of regime-switching models to momentary experience data pose particular data analytic challenges because time intervals between EMA measurements have varying lengths. We took the approach of discretizing and imputing gaps in the time series, but we expect that future statistical advances will contribute to improved implementation of regime-switching models with EMA data [3,13,25]. Future research may also wish to examine whether regime-switching measures derived from EMA ratings contribute to the prediction of outcomes beyond end of day ratings of least, worst, and average pain.…”

Section: Discussionmentioning

confidence: 99%

Temporal dynamics of pain: an application of regime-switching models to ecological momentary assessments in patients with rheumatic diseases

Schneider

Junghaenel

Ono

et al. 2018

Pain

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Advances in pain measurement using ecological momentary assessments offer novel opportunities for understanding the temporal dynamics of pain. This study examined whether regime-switching models, which capture processes characterized by recurrent shifts between different states, provide clinically relevant information for characterizing individuals based on their temporal pain patterns. Patients with rheumatic diseases (N = 116) provided 7 to 8 momentary pain ratings per day for 2 weekly periods, separated by 3 months. Regime-switching models extracted measures of Average pain (mean level over time), Amplitude (magnitude of shifts in pain levels), Persistence (average duration of pain states), and Dominance (relative duration of higher vs lower pain states) for each patient and assessment period. After controlling for Average pain, the Persistence of pain states uniquely predicted emotional functioning measures, whereas the Dominance of higher pain uniquely predicted physical functioning and pain interference. Longitudinal analyses of changes over the 3 months largely replicated cross-sectional results. Furthermore, patients' retrospective judgments of their pain were uniquely predicted by Amplitude and Dominance of higher pain states, and global impressions of change over the 3 months were predicted by changes on Dominance, controlling for Average pain levels. The results suggest that regime-switching models can usefully capture temporal dynamics of pain and can contribute to an improved measurement of patients' pain intensity.

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Section: Discussionmentioning

confidence: 99%

Temporal dynamics of pain: an application of regime-switching models to ecological momentary assessments in patients with rheumatic diseases

Schneider

Junghaenel

Ono

et al. 2018

Pain

View full text Add to dashboard Cite

show abstract

“…We chose the VAR(1) model, because it has become an extremely popular model for intensive longitudinal data (see Appendix E). HMMs are also a popular class of time series models in psychological research (Asparouhov et al, 2017;de Haan-Rietdijk et al, 2017;Neale et al, 2016;Visser, 2011) and the TVAR(1) model is an interesting extension of the VAR(1) model (De Haan-Rietdijk et al, 2016;Hamaker et al, 2009Hamaker et al, , 2010Hamaker et al, , 2016, which allows us to discuss how theoretical input can mitigate the problem of misspecification. Each of these models is misspecified, which means that the true system is not a special case of the model at hand.…”

Section: The Problem Of Misspecificationmentioning

confidence: 99%

Recovering Within-Person Dynamics from Psychological Time Series

Haslbeck

Ryan

2021

Multivariate Behavioral Research

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Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

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“…In particular, we compared the estimates for the two models to determine which one provided the most useful and realistic substantive description of the data. We concluded that the three-state model accounted for a more sensible and complete description of set-shifting performances (see [35] for a similar approach). The reader is referred to the Supplementary Material for a more detailed comparison of models’ estimates.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Dynamic Framework for Modelling Set-Shifting Performances

D’Alessandro

Lombardi

2019

Behavioral Sciences

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Higher-order cognitive functions can be seen as a class of cognitive processes which are crucial in situations requiring a flexible adjustment of behaviour in response to changing demands of the environment. The cognitive assessment of these functions often relies on tasks which admit a dynamic, or longitudinal, component requiring participants to flexibly adapt their behaviour during the unfolding of the task. An intriguing feature of such experimental protocols is that they allow the performance of an individual to change as the task unfolds. In this work, we propose a Latent Markov Model approach to capture some dynamic aspects of observed response patterns of both healthy and substance dependent individuals in a set-shifting task. In particular, data from a Wisconsin Card Sorting Test were analysed in order to represent performance trends in terms of latent cognitive states dynamics. The results highlighted how a dynamic modelling approach can considerably improve the amount of information a researcher, or a clinician, can obtain from the analysis of a set-shifting task.

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On the Use of Mixed Markov Models for Intensive Longitudinal Data

Cited by 37 publications

References 73 publications

Temporal dynamics of pain: an application of regime-switching models to ecological momentary assessments in patients with rheumatic diseases

Temporal dynamics of pain: an application of regime-switching models to ecological momentary assessments in patients with rheumatic diseases

Recovering Within-Person Dynamics from Psychological Time Series

A Dynamic Framework for Modelling Set-Shifting Performances

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