Improving convergence in growth mixture models without covariance structure constraints

Longitudinal analysis of the ABCD® study

The Adolescent Brain Cognitive Development® (ABCD) Study provides a unique opportunity to investigate developmental processes in a large, diverse cohort of youths, aged approximately 9-10 at baseline and assessed annually for 10 years. Given the size and complexity of the ABCD Study, researchers analyzing its data will encounter a myriad of methodological and analytical considerations. This review provides an examination of key concepts and techniques related to longitudinal analyses of the ABCD Study data, including: (1) characterization of the factors associated with variation in developmental trajectories; (2) assessment of how level and timing of exposures may impact subsequent development; (3) quantification of how variation in developmental domains may be associated with outcomes, including mediation models and reciprocal relationships. We emphasize the importance of selecting appropriate statistical models to address these research questions. By presenting the advantages and potential challenges of longitudinal analyses in the ABCD Study, this review seeks to equip researchers with foundational knowledge and tools to make informed decisions as they navigate and effectively analyze and interpret the multi-dimensional longitudinal data currently available.

Evidence That Growth Mixture Model Results Are Highly Sensitive to Scoring Decisions

Interest in identifying latent growth profiles to support the psychological and social-emotional development of individuals has translated into the widespread use of growth mixture models (GMMs). In most cases, GMMs are based on scores from item responses collected using survey scales or other measures. Research already shows that GMMs can be sensitive to departures from ideal modeling conditions and that growth model results outside of GMMs are sensitive to decisions about how item responses are scored, but the impact of scoring decisions on GMMs has never been investigated. We start to close that gap in the literature with the current study. Through empirical and Monte Carlo studies, we show that GMM results-including convergence, class enumeration, and latent growth trajectories within class-are extremely sensitive to seemingly arcane measurement decisions. Further, our results make clear that, because GMM latent classes are not known a priori, measurement models used to produce scores for use in GMMs are, almost by definition, misspecified because they cannot account for group membership. Misspecification of the measurement model then, in turn, biases GMM results. Practical implications of these results are discussed. Our findings raise serious concerns that many results in the current GMM literature may be driven, in part or whole, by measurement artifacts rather than substantive differences in developmental trends.

Chronic, increasing, and decreasing peer victimization trajectories and the development of externalizing and internalizing problems in middle childhood

Children's peer victimization trajectories and their longitudinal associations with externalizing and internalizing problems were investigated from Grades 2 to 5. Secondary data analysis was performed with the Early Childhood Longitudinal Study (ECLS-K-2011; n = 13,860, M age = 8.1 years old in the spring of Grade 2; 51.1% male, 46.7% White, 13.2% African-American, 25.3% Hispanic or Latino, 8.5% Asian, and 6.1% other or biracial). Children who experienced high and persistent levels of peer victimization (high-chronic victims) exhibited co-occurring externalizing and internalizing problems. Moreover, among high-chronic victims, boys had a more pronounced increase in their externalizing trajectories, and girls had greater increases in their social anxiety trajectories. In contrast, those with decreasing peer victimization across time exhibited signs of recovery, particularly with respect to their social anxiety. These findings elucidated how chronic, increasing, and decreasing victims exhibited distinct patterns in the co-occurring development of their externalizing and internalizing problems, and how findings varied depending on the form of problem behavior and by child sex.

Exploration of model misspecification in latent class methods for longitudinal data: Correlation structure matters

Modeling longitudinal trajectories and identifying latent classes of trajectories is of great interest in biomedical research, and software to identify latent classes of such is readily available for latent class trajectory analysis (LCTA), growth mixture modeling (GMM) and covariance pattern mixture models (CPMM). In biomedical applications, the level of within-person correlation is often non-negligible, which can impact the model choice and interpretation. LCTA does not incorporate this correlation. GMM does so through random effects, while CPMM specifies a model for within-class marginal covariance matrix. Previous work has investigated the impact of constraining covariance structures, both within and across classes, in GMMs-an approach often used to solve convergence problems. Using simulation, we focused specifically on how misspecification of the temporal correlation structure and strength, but correct variances, impacts class enumeration and parameter estimation under LCTA and CPMM. We found (1) even in the presence of weak correlation, LCTA often does not reproduce original classes, (2) CPMM performs well in class enumeration when the correct correlation structure is selected, and (3) regardless of misspecification of the correlation structure, both LCTA and CPMM give unbiased estimates of the class trajectory parameters when the within-individual correlation is weak and the number of classes is correctly specified. However, the bias increases markedly when the correlation is moderate for LCTA and when the incorrect correlation structure is used for CPMM. This work highlights the importance of correlation alone in obtaining appropriate model interpretations and provides insight into model choice.

Facilitating Growth Mixture Model Convergence in Preventive Interventions

Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post hoc adjustments to the model, particularly constraining covariance parameters to equality across classes. Methodological studies have shown that although convergence is improved with post hoc adjustments, they embed additional tenuous assumptions into the model that can adversely impact key aspects of the model such as number of classes extracted and the estimated growth trajectories in each class. To facilitate convergence without post hoc adjustments, this paper reviews the recent literature on covariance pattern mixture models, which approach GMMs from a marginal modeling tradition rather than the random effect modeling tradition used by traditional GMMs. We discuss how the marginal modeling tradition can avoid complexities in estimation encountered by GMMs that feature random effects, and we use data from a lifestyle intervention for increasing insulin sensitivity (a risk factor for type 2 diabetes) among 90 Latino adolescents with obesity to demonstrate our point. Specifically, GMMs featuring random effects-even with post hoc adjustments-fail to converge due to estimation errors, whereas covariance pattern mixture models following the marginal model tradition encounter no issues with estimation while maintaining the ability to answer all the research questions.

Association of BMI trajectories with cardiometabolic risk among low-income Mexican American children

BACKGROUND

The aim of this study was to identify distinct trajectories of BMI growth from 2 to 7.5 years and examine their associations with markers of cardiometabolic risk at age 7.5 years among a sample of low-income Mexican American children.

METHODS

This longitudinal cohort study recruited 322 mother-child dyads to participate prenatally and at child age 2, 3, 4.5, 6, and 7.5 years. Child height/weight, waist circumference, and blood pressure were assessed at each time point. Blood was collected from child at 7.5 years.

RESULTS

Covarying for birthweight, three BMI trajectories were identified: Low-Stable BMI (73% of the sample), High-Stable BMI (5.6% of the sample), and Increasing BMI over time (21.4% of the sample). The High-Stable and Increasing BMI classes had higher waist circumference and systolic blood pressure and lower HDL-c than the Low-Stable BMI class (ps < 0.05). Among children with BMIs below the 85th percentile, 16% had three or more cardiometabolic risk indicators.

CONCLUSIONS

BMI classes were consistent with existing literature. For youth, standard medical practice is to examine cardiometabolic risk indicators when BMI is high; however, this practice would miss 16% of youth in our sample who exhibit cardiometabolic risk but do not screen in based on BMI.

IMPACT

Research indicates Mexican American youth are at risk for cardiometabolic dysregulation relative to other ethnic groups, yet there is a paucity of longitudinal research. An Increasing BMI and a High-Stable BMI class were associated with larger waist circumference, higher systolic blood pressure, and lower HDL cholesterol than the Low-Stable BMI class. BMI trajectories in childhood predict cardiometabolic risk indicators. As the sole screener for deciding when to test cardiometabolic indicators, BMI alone will miss some children exhibiting cardiometabolic dysregulation.

Children's Daily Negative Affect Patterns and Food Consumption on Weekends: An Ecological Momentary Assessment Study

OBJECTIVE

This study evaluated the association between children's daily negative affect (NA) trajectories and unhealthy food consumption during weekends using ecological momentary assessment (EMA).

DESIGN

Children answered mobile phone-based EMA surveys 7 times a day for 2 weekend days per wave, with each survey assessing current NA and past 2-hour consumption of fried foods (chips or fries), sweets (pastries or sweets), and sugary beverages (drank soda or energy drinks).

SETTING

Los Angeles, California.

PARTICIPANTS

The sample consisted of 195 children (51% female; mean age, 9.65 years; SD, 0.93) from the Mothers and Their Children's Health cohort study.

MAIN OUTCOMES MEASURES

Negative affect trajectory (independent variable), unhealthy food consumption (dependent variable).

ANALYSIS

Latent growth mixture modeling classified NA trajectories across days and examined their association with unhealthy food consumption.

RESULTS

The latent growth mixture modeling identified 3 classes of daily NA trajectories: (1) stable low, (2) early increasing and late decreasing and (3) early decreasing and late increasing. Fried food consumption was higher on early increasing and late decreasing and early decreasing and late increasing NA trajectories than days with stable low NA.

CONCLUSIONS AND IMPLICATIONS

By better understanding day-to-day variability in children's affect and eating, we can individually tailor obesity interventions to account for the emotional contexts in which unhealthy eating occurs.

Longitudinal Trajectories of Quality of Life Among People With Mild-to-Moderate Dementia: A Latent Growth Model Approach With IDEAL Cohort Study Data

OBJECTIVES

We aimed to examine change over time in self-rated quality of life (QoL) in people with mild-to-moderate dementia and identify subgroups with distinct QoL trajectories.

METHODS

We used data from people with mild-to-moderate dementia followed up at 12 and 24 months in the Improving the experience of Dementia and Enhancing Active Life (IDEAL) cohort study (baseline n = 1,537). A latent growth model approach examined mean change over time in QoL, assessed with the QoL-AD scale, and investigated associations of baseline demographic, cognitive, and psychological covariates with the intercept and slope of QoL. We employed growth mixture modeling to identify multiple growth trajectories.

RESULTS

Overall mean QoL scores were stable and no associations with change over time were observed. Four classes of QoL trajectories were identified: 2 with higher baseline QoL scores, labeled Stable (74.9%) and Declining (7.6%), and 2 with lower baseline QoL scores, labeled Stable Lower (13.7%) and Improving (3.8%). The Declining class had higher baseline levels of depression and loneliness, and lower levels of self-esteem and optimism, than the Stable class. The Stable Lower class was characterized by disadvantage related to social structure, poor physical health, functional disability, and low psychological well-being. The Improving class was similar to the Stable Lower class but had lower cognitive test scores.

DISCUSSION

Understanding individual trajectories can contribute to personalized care planning. Efforts to prevent decline in perceived QoL should primarily target psychological well-being. Efforts to improve QoL for those with poorer QoL should additionally address functional impairment, isolation, and disadvantage related to social structure.

Post-traumatic growth trajectories among frontline healthcare workers during the COVID-19 pandemic: A three-wave follow-up study in mainland China

OBJECTIVES

The COVID-19 pandemic has taken a significant toll on people worldwide for more than 2 years. Previous studies have highlighted the negative effects of COVID-19 on the mental health of healthcare workers (HCWs) more than the positive changes, such as post-traumatic growth (PTG). Furthermore, most previous studies were cross-sectional surveys without follow-ups. This study draws on PTG follow-up during the COVID-19 outbreak at 12-month intervals for 2 years since 2020. The trajectories and baseline predictors were described.

METHODS

A convenience sampling method was used to recruit frontline nurses or doctors at the COVID-19-designated hospital who were eligible for this study. A total of 565 HCWs completed the 2 years follow-up and were used for final data analysis. The latent growth mixture models (GMM) was used to identify subgroups of participants with different PTG trajectories. Multinomial logistic regression model was used to find predictors among sociodemographic characteristics and resilience at baseline.

RESULTS

Four trajectory PTG types among HCWs were identified: 'Persistent, "Steady increase", "High with drop", and "Fluctuated rise." Comparing the "Persistent low" type, the other three categories were all associated with older age, higher education. Furthermore, "Persistent low" was also negatively associated with resilience at baseline.

CONCLUSION

The PTG of HCWs with different characteristics showed different trends over time. It is necessary to increase the measure frequency to understand the PTG status in different times. Improving HCW's resilience could help improve staff PTG.