Impact of Serial Correlation Misspecification with the Linear Mixed Model

Linear mixed models are popular models for use with clustered and longitudinal data due to their ability to model variation at different levels of clustering. A Monte Carlo study was used to explore the impact of assumption violations on the bias of parameter estimates and the empirical type I error rates. Simulated conditions included in this study are: simulated serial correlation structure, fitted serial correlation structure, random effect distribution, cluster sample size, and number of measurement occasions. Results showed that the fixed effects are unbiased, but the random components tend to be overestimated and the empirical Type I error rates tend to be inflated. Implications for applied researchers were discussed

Defining a recovery-oriented cascade of care for opioid use disorder: A community-driven, statewide cross-sectional assessment

Background In light of the accelerating and rapidly evolving overdose crisis in the United States (US), new strategies are needed to address the epidemic and to efficiently engage and retain individuals in care for opioid use disorder (OUD). Moreover, there is an increasing need for novel approaches to using health data to identify gaps in the cascade of care for persons with OUD. Methods and findings Between June 2018 and May 2019, we engaged a diverse stakeholder group (including directors of statewide health and social service agencies) to develop a statewide, patient-centered cascade of care for OUD for Rhode Island, a small state in New England, a region highly impacted by the opioid crisis. Through an iterative process, we modified the cascade of care defined by Williams et al. for use in Rhode Island using key national survey data and statewide health claims datasets to create a cross-sectional summary of 5 stages in the cascade. Approximately 47,000 Rhode Islanders (5.2%) were estimated to be at risk for OUD (stage 0) in 2016. At the same time, 26,000 Rhode Islanders had a medical claim related to an OUD diagnosis, accounting for 55% of the population at risk (stage 1); 27% of the stage 0 population, 12,700 people, showed evidence of initiation of medication for OUD (MOUD, stage 2), and 18%, or 8,300 people, had evidence of retention on MOUD (stage 3). Imputation from a national survey estimated that 4,200 Rhode Islanders were in recovery from OUD as of 2016, representing 9% of the total population at risk. Limitations included use of self-report data to arrive at estimates of the number of individuals at risk for OUD and using a national estimate to identify the number of individuals in recovery due to a lack of available state data sources. Conclusions Our findings indicate that cross-sectional summaries of the cascade of care for OUD can be used as a health policy tool to identify gaps in care, inform data-driven policy decisions, set benchmarks for quality, and improve health outcomes for persons with OUD. There exists a significant opportunity to increase engagement prior to the initiation of OUD treatment (i.e., identification of OUD symptoms via routine screening or acute presentation) and improve retention and remission from OUD symptoms through improved community-supported processes of recovery. To do this more precisely, states should work to systematically collect data to populate their own cascade of care as a health policy tool to enhance system-level interventions and maximize engagement in care

Creating a Developmental Scale to Account for Heterotypic Continuity in Development: A Simulation Study.

Many psychological constructs show heterotypic continuity-their behavioral manifestations change with development but their meaning remains the same. However, research has paid little attention to how to account for heterotypic continuity. A promising approach to account for heterotypic continuity is creating a developmental scale using vertical scaling. A simulation was conducted to compare creating a developmental scale using vertical scaling to traditional approaches of longitudinal assessment. Traditional approaches that failed to account for heterotypic continuity resulted in less accurate growth estimates, at the person- and group level. Findings suggest that ignoring heterotypic continuity may result in faulty developmental inferences. Creating a developmental scale with vertical scaling is recommended to link different measures across time and account for heterotypic continuity

Reactive and Control Processes in the Development of Internalizing and Externalizing Problems Across Early Childhood to Adolescence

Reactive and control processes—e.g., negative emotionality and immediacy preference—may predict distinct psychopathology trajectories. However, externalizing and internalizing problems change in behavioral manifestation across development and across contexts, thus necessitating the use of different measures and informants across ages. This is the first study that created developmental scales for both internalizing and externalizing problems by putting scores from different informants and measures onto the same scale to examine temperament facets as risk factors. Multidimensional linking allowed us to examine trajectories of internalizing and externalizing problems from ages 2 to 15 years (N = 1,364) using near-annual ratings by mothers, fathers, teachers, other caregivers, and self-report. We examined reactive and control processes in early childhood as predictors of the trajectories and as predictors of general versus specific psychopathology in adolescence. Negative emotionality at age 4 predicted general psychopathology and unique externalizing problems at age 15. Wait times on an immediacy preference task at age 4 were negatively associated with age 15 general psychopathology, and positively associated with unique internalizing problems. Findings demonstrate the value of developmental scaling for examining development of psychopathology across a lengthy developmental span and the importance of considering reactive and control processes in development of psychopathology

Studying a Moving Target in Development: The Challenge and Opportunity of Heterotypic Continuity.

Many psychological constructs show heterotypic continuity-their behavioral manifestations change with development but their meaning remains the same (e.g., externalizing problems). However, research has paid little attention to how to account for heterotypic continuity. Conceptual and methodological challenges of heterotypic continuity may prevent researchers from examining lengthy developmental spans. Developmental theory requires that measurement accommodate changes in manifestation of constructs. Simulation and empirical work demonstrate that failure to account for heterotypic continuity when collecting or analyzing longitudinal data results in faulty developmental inferences. Accounting for heterotypic continuity may require using different measures across time with approaches that link measures on a comparable scale. Creating a developmental scale (i.e., developmental scaling) is recommended to link measures across time and account for heterotypic continuity, which is crucial in understanding development across the lifespan. The current synthesized review defines heterotypic continuity, describes how to identify it, and presents solutions to account for it. We note challenges of addressing heterotypic continuity, and propose steps in leveraging opportunities it creates to advance empirical study of development

Studying a Moving Target in Development: The Challenge and Opportunity of Heterotypic Continuity.

Many psychological constructs show heterotypic continuity-their behavioral manifestations change with development but their meaning remains the same (e.g., externalizing problems). However, research has paid little attention to how to account for heterotypic continuity. Conceptual and methodological challenges of heterotypic continuity may prevent researchers from examining lengthy developmental spans. Developmental theory requires that measurement accommodate changes in manifestation of constructs. Simulation and empirical work demonstrate that failure to account for heterotypic continuity when collecting or analyzing longitudinal data results in faulty developmental inferences. Accounting for heterotypic continuity may require using different measures across time with approaches that link measures on a comparable scale. Creating a developmental scale (i.e., developmental scaling) is recommended to link measures across time and account for heterotypic continuity, which is crucial in understanding development across the lifespan. The current synthesized review defines heterotypic continuity, describes how to identify it, and presents solutions to account for it. We note challenges of addressing heterotypic continuity, and propose steps in leveraging opportunities it creates to advance empirical study of development