Extending the mixed‐effects model to consider within‐subject variance for Ecological Momentary Assessment data

Nordgren, Rachel; Hedeker, Donald; Dunton, Genevieve F.; Yang, Chih‐Hsiang

doi:10.1002/sim.8429

Cited by 28 publications

(32 citation statements)

References 31 publications

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“…To examine our first objective (i.e., the impact of childhood abuse on affect intensity and variability), we conducted a series of mixed‐effects LSMs. The LSM is an extension of random‐intercept mixed‐effects regression in which log‐linear submodels are included to allow independent predictors of both the between‐ and within‐person variance estimates (Dzubur et al., 2020; Hedeker et al., 2008; Nordgren et al., 2020). In the LSM, the random location (i.e., intercept) reflects the degree to which a participant deviates from the population mean and the random scale (i.e., participant‐specific, within‐person variance term) reflects the degree to which a participant deviates from their own means.…”

Section: Methodsmentioning

confidence: 99%

Affective variability and childhood abuse increase the risk for nonsuicidal self‐injury following psychiatric hospitalization

Brick

Nugent

Armey

2021

Journal of Traumatic Stress

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Childhood abuse is associated with myriad negative behavioral health outcomes, including nonsuicidal self‐injury (NSSI). Models aimed at understanding the mechanism whereby childhood abuse may exert negative effects frequently highlight alterations or dysregulations in experienced affect, particularly during times of stress. The period immediately following discharge from psychiatric hospitalization represents a time of risk for individuals experiencing NSSI. We aimed to investigate the extent to which childhood abuse predicts affective variability and examine whether affective variability predicts who will experience NSSI thoughts or behaviors in the 6 months following hospital discharge. Participants were adults (N = 133) engaged in a 3‐week ecological momentary assessment study immediately following psychiatric hospitalization who returned for a 6‐month follow‐up. Location scale modeling (LSM) was used to model the impact of childhood abuse on affect variability; estimates of affect variability were then resampled and examined as risk factors for NSSI at follow‐up. Overall, individuals who experienced childhood abuse reported lower positive affect intensity. Participants exposed to emotional and sexual abuse reported less between‐person variability in positive affect, and those who experienced sexual abuse evidenced higher between‐person variability in negative affect. Models indicated that higher overall negative affect was associated with a higher NSSI risk, ORs ∼ 1.82–2.10 even after accounting for childhood abuse and lifetime NSSI. These findings indicate the importance of in vivo affect as a critical influence on behavior during high‐risk periods, reinforcing the need to move beyond traditional cross‐sectional approaches of assessment, analysis, and treatment.

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Section: Methodsmentioning

confidence: 99%

Affective variability and childhood abuse increase the risk for nonsuicidal self‐injury following psychiatric hospitalization

Brick

Nugent

Armey

2021

Journal of Traumatic Stress

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“…Equations () to () correspond with the MELS as suggested in References 6, 8, 9, 18, and 19 and also see Reference 20 and when we assume that there is only one Level 1 predictor whose regression weight can vary between persons (as in the linear growth model example), they would match with the MELS for which an ML estimator was derived in Reference 17. A problem with this formulation of the MELS is that the Level 1 residuals may additionally be autocorrelated over time and this is not considered in the model.…”

Section: Mixed‐effects Location Scale Modelmentioning

confidence: 96%

“…Furthermore, a Bayesian approach is used in all cases to estimate the model parameters. The reason for the latter is that a maximum likelihood (ML) estimator for the MELS—at least to our knowledge—has so far only been derived for the case of a mixed‐effects model in which the intercept and the weight of a single Level 1 predictor is allowed to vary between participants (see Reference 17). Thus, if researchers want to use an ML estimator for the MELS, they then can currently estimate no model in which the weights of several predictors vary between individuals, in which the errors are autocorrelated, and/or in which there are also between‐person differences in this autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation

Nestler

2021

Statistics in Medicine

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Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed‐effects model that allows to examine hypotheses concerning between‐person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that—in addition to the random effects for the mean structure—also includes a random effect for the within‐subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach.

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“…A two-stage data analysis approach using the MixWILD program was applied in the current study, in which the random effects estimated at the first stage were used as predictors in a second-stage model [17,18]. Specifically, statistical models examined whether random subject effects (i.e., intercept and slope) of MVPA minutes surrounding the EMA prompt (during baseline) predicted future average daily MVPA minutes (at 6 month follow-up).…”

Section: Statistical Modelmentioning

confidence: 99%

“…To date, statistical models have been limited in their ability to test whether subject-level slopes (i.e., within-subject associations between time-varying predictors and time-varying outcomes) are associated with subject-level outcomes (e.g., overall physical activity levels or the presence of a disease) using intensive longitudinal data. The recent development of a two-stage joint modeling approach combining a mixed-effects model (at Stage 1) with a single-level linear regression model (at Stage 2) using the MixWILD program enables the identification of subject-level parameters (i.e., random effects) to predict health outcomes at the same subject level [17,18]. MixWILD is an opensource and user-friendly program (with point-andclick graphical user interface) available to the public for analyzing intensive longitudinal data (available at https://reach-lab.github.io/MixWildGUI/).…”

Section: Introductionmentioning

confidence: 99%

An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

Yang

Maher

Ponnada

et al. 2020

Translational Behavioral Medicine

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People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

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Extending the mixed‐effects model to consider within‐subject variance for Ecological Momentary Assessment data

Cited by 28 publications

References 31 publications

Affective variability and childhood abuse increase the risk for nonsuicidal self‐injury following psychiatric hospitalization

Affective variability and childhood abuse increase the risk for nonsuicidal self‐injury following psychiatric hospitalization

An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation

An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

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