Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study.

Objectives: The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor.

Methods: Wrist actigraphy measures of participants (N = 289, M = 77.

Discrimination and Education Quality Moderate the Association of Sleep With Cognitive Function in Older Black Adults: Results From the Einstein Aging Study.

Objectives: Heterogeneity among Black adults' experiences of discrimination and education quality independently influence cognitive function and sleep, and may also influence the extent to which sleep is related to cognitive function. We investigated the effect of discrimination on the relationship between objective sleep characteristics and cognitive function in older Black adults with varying education quality.

Method: Cross-sectional analyses include Black participants in the Einstein Aging Study (N = 104, mean age = 77.

Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus.

The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework.

Child Effects on Parental Negativity: The Role of Heritable and Prenatal Factors.

This study examined two possible mechanisms, evocative gene-environment correlation and prenatal factors, in accounting for child effects on parental negativity. Participants included 561 children adopted at birth, and their adoptive parents and birth parents within a prospective longitudinal adoption study. Findings indicated child effects on parental negativity, such that toddlers' negative reactivity at 18 months was positively associated with adoptive parents' over-reactive and hostile parenting at 27 months.

A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes.

Intensive longitudinal designs involving repeated assessments of constructs often face the problems of nonignorable attrition and selected omission of responses on particular occasions. However, time series models, such as vector autoregressive (VAR) models, are often fit to these data without consideration of nonignorable missingness. We introduce a Bayesian model that simultaneously represents the over-time dependencies in multivariate, multiple-subject time series data via a VAR model, and possible ignorable and nonignorable missingness in the data.

dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling.

Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios.

Handling Missing Data in the Modeling of Intensive Longitudinal Data.

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters.