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

• Published in: Behavior research methods Vol. 52; no. 4; pp. 1403 - 1427
• Main Authors: Dzubur, Eldin, Ponnada, Aditya, Nordgren, Rachel, Yang, Chih-Hsiang, Intille, Stephen, Dunton, Genevieve, Hedeker, Donald
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020
• Subjects: Bayes Theorem; Behavioral Science and Psychology; Biomedical Research; Biometry; Cognitive Psychology; Logistic Models; Longitudinal Studies; Psychology; Research Design; Software; Variance modeling; Ecological momentary assessment; Intensive longitudinal data; Multilevel; Mixed models; Heteroscedasticity
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-019-01322-1

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.