A 3‐level Bayesian mixed effects location scale model with an application to ecological momentary assessment data
Open accessAbstract
Ecological momentary assessment studies usually produce intensively measured longitudinal data with large numbers of observations per unit, and research interest is often centered around understanding the changes in variation of people's thoughts, emotions and behaviors. Hedeker et al developed a 2‐level mixed effects location scale model that allows observed covariates as well as unobserved variables to influence both the mean and the within‐subjects variance, for a 2‐level data structure where observations are nested within subjects. In some ecological momentary assessment studies, subjects are measured at multiple waves, and within each wave, subjects are measured over time. Li and Hedeker extended the original 2‐level model to a 3‐level data structure where observations are nested within days and days are then nested within subjects, by including a random location and scale intercept at the intermediate wave level. However, the 3‐level random intercept model assumes constant response change rate for both the mean and variance. To account for changes in variance across waves, as well as clustering attributable to waves, we propose a more comprehensive location scale model that allows subject heterogeneity at baseline as well as across different waves, for a 3‐level data structure where observations are nested within waves and waves are then further nested within subjects. The model parameters are estimated using Markov chain Monte Carlo methods. We provide details on the Bayesian estimation approach and demonstrate how the Stan statistical software can be used to sample from the desired distributions and achieve consistent estimates. The proposed model is validated via a series of simulation studies. Data from an adolescent smoking study are analyzed to demonstrate this approach. The analyses clearly favor the proposed model and show significant subject heterogeneity at baseline as well as change over time, for both mood mean and variance. The proposed 3‐level location scale model can be widely applied to areas of research where the interest lies in the consistency in addition to the mean level of the responses.
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Due to the implementation of novel statistical procedures, a balance between research necessities and clinical utility could be achieved.
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From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [90]
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Cites
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835 · see citing works
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
<i>Stan</i> : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · see citing works
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
(2015) · doi:10.1007/s11222-016-9696-4 · Cited by 5,611 · see citing works
Diary Methods: Capturing Life as it is Lived
Bolger, N., Davis, A., & Rafaeli, E. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145030 · Cited by 3,843 · see citing works
Estimation and Inference in Nonlinear Structural ModelsPreprint
Berndt, E. R., Hall, B. H., Hall, R. E., & Hausman, J. A. (1974) · RePEc: Research Papers in Economics · Cited by 2,094 · see citing works
Ecological Momentary Assessment (Ema) in Behavioral Medicine
Stone, A. A., & Shiffman, S. (1994) · Annals of Behavioral Medicine · doi:10.1093/abm/16.3.199 · Cited by 1,931 · see citing works
Bayesian Model Choice Via Markov Chain Monte Carlo Methods
Carlin, B., & Chib, S. (1995) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1995.tb02042.x · Cited by 1,137 · see citing works
Estimating Regression Models with Multiplicative Heteroscedasticity
Harvey, A. C. (1976) · Econometrica · doi:10.2307/1913974 · Cited by 966 · see citing works
Longford, N. T. (2011) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-642-04898-2_471 · Cited by 751 · see citing works
Strategies for analyzing ecological momentary assessment data.
Schwartz, J. E., & Stone, A. A. (1998) · Health Psychology · doi:10.1037//0278-6133.17.1.6 · Cited by 631 · see citing works
Jahng, S., Wood, P. K., & Trull, T. J. (2008) · Psychological Methods · doi:10.1037/a0014173 · Cited by 593 · see citing works
Ecological momentary assessment of mood disorders and mood dysregulation
Ebner-Priemer, U. W., & Trull, T. J. (2009) · Psychological Assessment · doi:10.1037/a0017075 · Cited by 530 · see citing works
Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data
Neuhaus, J. M., & Kalbfleisch, J. D. (1998) · Biometrics · doi:10.2307/3109770 · Cited by 528 · see citing works
Strategies for analyzing ecological momentary assessment data.
Schwartz, J. E., & Stone, A. A. (1998) · Health Psychology · doi:10.1037/0278-6133.17.1.6 · Cited by 484 · see citing works
Ecological momentary assessment research in behavioral medicine
Smyth, J. M., & Stone, A. A. (2003) · Journal of Happiness Studies · doi:10.1023/a:1023657221954 · Cited by 439 · see citing works
The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models
Hobert, J. P., & Casella, G. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476714 · Cited by 435 · see citing works
Models for Intensive Longitudinal Data
(2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195173444.001.0001 · Cited by 367 · see citing works
Hedeker, D., Mermelstein, R. J., & Demirtaş, H. (2007) · Biometrics · doi:10.1111/j.1541-0420.2007.00924.x · Cited by 333 · see citing works
Self-citationModelling Variance Heterogeneity in Normal Regression Using GLIM
Aitkin, M. A. (1987) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347792 · Cited by 287 · see citing works
Bayesian inference for generalized linear mixed models
Fong, Y., Rue, H., & Wakefield, J. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp053 · Cited by 287 · see citing works
The Experience of Psychopathology: Investigating Mental Disorders in their Natural Settings
Csíkszentmihályi, M. (2006) · Medical Entomology and Zoology · Cited by 237 · see citing works
Random Effects Probit and Logistic Regression Models for Three-Level Data
Gibbons, R. D., & Hedeker, D. (1997) · Biometrics · doi:10.2307/2533520 · Cited by 206 · see citing works
Self-citationHedeker, D., Mermelstein, R. J., & Demirtaş, H. (2012) · Statistics in Medicine · doi:10.1002/sim.5338 · Cited by 184 · see citing works
Self-citationGeneralized Linear Mixed Models
McCulloch, C. E., & Neuhaus, J. M. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a10021 · Cited by 145 · see citing works
Shenk, T. M., White, G. C., & Burnham, K. P. (1998) · Ecological Monographs · doi:10.2307/2657247 · Cited by 144 · see citing works
Hedeker, D., Mermelstein, R. J., Berbaum, M. L., & Campbell, R. T. (2009) · Addiction · doi:10.1111/j.1360-0443.2008.02435.x · Cited by 139 · see citing works
Self-citationHyperglycemia and Perioperative Glucose Management
Duncan, A. E. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212803832236 · Cited by 119 · see citing works
Singer, B. H. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1502.pub2 · Cited by 98 · see citing works
Modeling Heterogeneous Variance–Covariance Components in Two-Level Models
Leckie, G., French, R., Charlton, C., & Browne, W. J. (2014) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998614546494 · Cited by 89 · see citing works
THE DISTRIBUTION OF PHENOTYPIC VARIANCE WITH INBREEDING
Fowler, K., & Whitlock, M. C. (1999) · Evolution · doi:10.1111/j.1558-5646.1999.tb04528.x · Cited by 88 · see citing works
Dynamic typology clustering within the stages of change for smoking cessation
Norman, G. J., Velicer, W. F., Fava, J. L., & Prochaska, J. O. (1998) · Addictive Behaviors · doi:10.1016/s0306-4603(97)00039-7 · Cited by 81 · see citing works
Rast, P., Hofer, S. M., & Sparks, C. (2012) · Multivariate Behavioral Research · doi:10.1080/00273171.2012.658328 · Cited by 78 · see citing works
de Vries, M. W., & Csíkszentmihályi, M. (1992) · Cited by 75 · see citing works
The Experience of Psychopathology edited by Marten W. de Vries
Csíkszentmihályi, M. (1992) · Cited by 75 · see citing works
Simulation driven inferences for multiply imputed longitudinal datasets*
Demirtas, H. (2004) · Statistica Neerlandica · doi:10.1111/j.1467-9574.2004.00271.x · Cited by 74 · see citing works
Prediction of tropospheric scintillation on satellite links from radiosonde data
Vasseur, H. (1999) · IEEE Transactions on Antennas and Propagation · doi:10.1109/8.761069 · Cited by 74 · see citing works
Ecological momentary assessment of adolescent smoking cessation: A feasibility study
Gwaltney, C. J., Bartolomei, R. J., Colby, S. M., & Kahler, C. W. (2008) · Nicotine & Tobacco Research · doi:10.1080/14622200802163118 · Cited by 69 · see citing works
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Demirtaş, H., Hedeker, D., & Mermelstein, R. J. (2009) · Statistics and Its Interface · doi:10.4310/sii.2009.v2.n4.a1 · Cited by 69 · see citing works
Self-citationA mixed-effects regression model for three-level ordinal response data
Raman, R., & Hedeker, D. (2005) · Statistics in Medicine · doi:10.1002/sim.2186 · Cited by 54 · see citing works
Self-citationTime Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability
von Oertzen, T., & Boker, S. M. (2009) · Psychometrika · doi:10.1007/s11336-009-9137-9 · Cited by 47 · see citing works
Estimating Dynamical Systems: Derivative Estimation Hints From Sir Ronald A. Fisher
Deboeck, P. R. (2010) · Multivariate Behavioral Research · doi:10.1080/00273171.2010.498294 · Cited by 47 · see citing works
Li, X., & Hedeker, D. (2012) · Statistics in Medicine · doi:10.1002/sim.5393 · Cited by 39 · see citing works
Self-citationEcological momentary assessment in addiction
Lukasiewicz, M., Fareng, M., Benyamina, A., Blecha, L., Reynaud, M., & Falissard, B. (2007) · Expert Review of Neurotherapeutics · doi:10.1586/14737175.7.8.939 · Cited by 37 · see citing works
An exploration of methodological pluralism in nursing research
Baker, C., Norton, S. A., YOUNG, P. K., & Ward, S. E. (1998) · Research in Nursing & Health · doi:10.1002/(sici)1098-240x(199812)21:6 · Cited by 36 · see citing works
A Bayesian Approach to the Linear Model with Unequal Variances
Leonard, T. (1975) · Technometrics · doi:10.1080/00401706.1975.10489277 · Cited by 36 · see citing works
Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit‐Specific Outcomes
Gueorguieva, R. V. (2005) · Biometrics · doi:10.1111/j.1541-020x.2005.00409_1.x · Cited by 34 · see citing works
Goldstein, H., Leckie, G., Charlton, C. M. J., Tilling, K. M., & Browne, W. J. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217706728 · Cited by 30 · see citing works
Piasecki, T. M., Trela, C. J., Hedeker, D., & Mermelstein, R. J. (2013) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntt132 · Cited by 28 · see citing works
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Kapur, K., Li, X., Blood, E., & Hedeker, D. (2014) · Statistics in Medicine · doi:10.1002/sim.6345 · Cited by 24 · see citing works
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Dewey, D., McDonald, M. K., Brown, W. J., Boyd, S. J., Bunnell, B. E., & Schuldberg, D. (2015) · Psychiatry Research · doi:10.1016/j.psychres.2015.09.009 · Cited by 23 · see citing works
A potentially useful distribution model for dietary intake data
Myles, J. P., Price, G., Hunter, N., Day, M., & Duffy, S. W. (2003) · Public Health Nutrition · doi:10.1079/phn2003459 · Cited by 14 · see citing works
Random Location and Scale Effects: Model Building Methods for a General Class of Models
Cleveland, W. S., Denby, L., & Liu, C. (2000) · Cited by 9 · see citing works
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Geukes, K., Nestler, S., Hutteman, R., Küfner, A. C. P., & Back, M. D. (2017) · Journal of Research in Personality · doi:10.1016/j.jrp.2016.06.003 · Cited by 210 · see citing works
Colombo, D., Fernández-Álvarez, J., Patané, A., Semonella, M., Kwiatkowska, M., García-Palacios, A., Cipresso, P., Riva, G., & Botella, C. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8040465 · Cited by 194 · see citing works
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Kooken, J. W. (2015) · OpenCommons at University of Connecticut (University of Connecticut)
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Self-citationWu, B. P., & Hedeker, D. (2026) · Statistics in Medicine · doi:10.1002/sim.70640
Self-citation
- Authors
- Lin, X., Mermelstein, R. J., & Hedeker, D.
- Affiliations
- University of Chicago (US)
- University of Illinois Chicago (US)
- Corresponding author
- Hedeker, Donald
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2018-02-26
- Volume
- 37
- Issue
- 13
- Pages
- 2108-2119
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC5980691
- OpenAlex
- W2100612361
- Cited by
- 48
- References
- 55
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 8 · 2021: 2 · 2020: 8 · 2019: 6 · 2018: 3 · 2017: 2 · 2016: 3 · 2015: 1 · 2014: 2 · 2013: 1
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01 HL121330)
- National Cancer Institute (5P01CA09862)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- location-scale models, ecological momentary assessment, within-subject variance, subject heterogeneity, nested data, longitudinal data, Bayesian estimation, Stan, adolescent smoking, mood, response consistency
- MeSH terms
- Ecological Momentary Assessment, Adolescent, Bayes Theorem, Humans, Smoking, Models, Statistical
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