A shared parameter location scale mixed effect model for EMA data subject to informative missing

Lin, Xiaolei; Mermelstein, Robin J.; Hedeker, Donald

doi:10.1007/s10742-018-0184-5

Cited by 12 publications

(15 citation statements)

References 15 publications

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“…MELS models are a relatively recent development in the long history of different approaches to modeling heterogeneous variances (e.g., Aitkin, 1987;Bryk & Raudenbush, 1988;Culpepper, 2010;Goldstein, 2011;Harvey, 1976;Leonard, 1975;Lindley, 1971;Pinheiro & Bates, 2000;Raudenbush, 1988). Researchers have discussed the development of these models in statistical journals and texts (e.g., Goldstein, 2011;Hedeker, Mermelstein, & Demirtas, 2008;Lin, Mermelstein, & Hedeker, 2018a, 2018bWalters, Hoffman, & Templin, 2018) and applied these models (most often) in fields where the collection of intensive longitudinal data is more common (e.g., medicine; Pugach, Hedeker, Richmond, Sokolovsky, & Mermelstein, 2014). For example, Watts, Walters, Hoffman, and Templin (2016) examined whether time-invariant (Level 2 predictors, e.g., gender, age, Alzheimer's disease status) as well as time-varying predictors (Level 1 predictors, e.g., day monitor worn) were associated with individual differences in mean level (location side) as well as intraindividual variability (scale side) of physical activity.…”

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confidence: 99%

From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances

Lester

Cullen-Lester

Walters

2019

Organizational Research Methods

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Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.

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confidence: 99%

From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances

Lester

Cullen-Lester

Walters

2019

Organizational Research Methods

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“…Recently, shared‐parameter mixed effects models have been developed to relax the MAR assumption in EMA data analysis 13,38 . Such models assume conditional independence of missingness and unobserved data given random effects.…”

Section: Discussionmentioning

confidence: 99%

A tractable method to account for high‐dimensional nonignorable missing data in intensive longitudinal data

Yuan

Hedeker

Mermelstein

et al. 2020

Statistics in Medicine

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Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing-data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high-dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing-data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second-order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed-form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U-shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.

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“…In this approach, a subject's nonresponse probability is assumed to be associated with the mean of a subject's mood level. As mood variance is also informative, Lin et al went further and proposed a shared‐parameter location‐scale mixed model to link the missingness process to the mean as well as the variance function of the mood outcome via the shared subject‐level random intercepts 8 …”

Section: Introductionmentioning

confidence: 99%

A shared‐parameter location‐scale mixed model to link the responsivity in self‐initiated event reports and the event‐contingent Ecological Momentary Assessments

Mermelstein

Hedeker

2022

Statistics in Medicine

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We address the issue of (non-) responsivity of self-initiated assessments in Ecological Momentary Assessment (EMA) or other mobile health (mHealth) studies, where subjects are instructed to self-initiate reports when experiencing defined events, for example, smoking. Since such reports are self-initiated, the frequency and determinants of nonresponse to these event reports is usually unknown, however it may be suspected that nonresponse of such self-initiated reports is not random. In this case, existing methods for missing data may be insufficient in the modeling of these observed self-initiated reports. In certain EMA studies, random prompts, distinct from the self-initiated reports, may be converted to event reports. For example, such a conversion can occur if during a random prompt a subject is assessed about the event (eg, smoking) and it is determined that the subject is engaging in the event at the time of the prompt. Such converted prompts can provide some information about the subject's non-responsivity of event reporting. Furthermore, such non-responsivity can be associated with the primary longitudinal EMA outcome (eg, mood) in which case a joint modeling of the non-responsivity and the mood outcome is possible. Here, we propose a shared-parameter location-scale model to link the primary outcome model for mood and a model for subjects' non-responsivity by shared random effects which characterize a subject's mood level, mood change pattern, and mood variability. Via simulations and real data analysis, our proposed model is shown to be more informative, have better coverage of parameters, and provide better fit to the data than more conventional models.

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A shared parameter location scale mixed effect model for EMA data subject to informative missing

Cited by 12 publications

References 15 publications

From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances

From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances

A tractable method to account for high‐dimensional nonignorable missing data in intensive longitudinal data

A shared‐parameter location‐scale mixed model to link the responsivity in self‐initiated event reports and the event‐contingent Ecological Momentary Assessments

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