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MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data

The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the cap...

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Autores principales: Dzubur, Eldin, Ponnada, Aditya, Nordgren, Rachel, Yang, Chih-Hsiang, Intille, Stephen, Dunton, Genevieve, Hedeker, Donald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406537/
https://www.ncbi.nlm.nih.gov/pubmed/31898295
http://dx.doi.org/10.3758/s13428-019-01322-1
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author Dzubur, Eldin
Ponnada, Aditya
Nordgren, Rachel
Yang, Chih-Hsiang
Intille, Stephen
Dunton, Genevieve
Hedeker, Donald
author_facet Dzubur, Eldin
Ponnada, Aditya
Nordgren, Rachel
Yang, Chih-Hsiang
Intille, Stephen
Dunton, Genevieve
Hedeker, Donald
author_sort Dzubur, Eldin
collection PubMed
description The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject’s random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton–Raphson solution. The mean and variance of each individual’s random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-019-01322-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-74065372020-08-13 MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data Dzubur, Eldin Ponnada, Aditya Nordgren, Rachel Yang, Chih-Hsiang Intille, Stephen Dunton, Genevieve Hedeker, Donald Behav Res Methods Article The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject’s random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton–Raphson solution. The mean and variance of each individual’s random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-019-01322-1) contains supplementary material, which is available to authorized users. Springer US 2020-01-02 2020 /pmc/articles/PMC7406537/ /pubmed/31898295 http://dx.doi.org/10.3758/s13428-019-01322-1 Text en © The Author(s) 2019 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Dzubur, Eldin
Ponnada, Aditya
Nordgren, Rachel
Yang, Chih-Hsiang
Intille, Stephen
Dunton, Genevieve
Hedeker, Donald
MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title_full MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title_fullStr MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title_full_unstemmed MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title_short MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
title_sort mixwild: a program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406537/
https://www.ncbi.nlm.nih.gov/pubmed/31898295
http://dx.doi.org/10.3758/s13428-019-01322-1
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