Integrating developmental theory and methodology: Using derivatives to articulate change theories, models, and inferences

College Students' prescription drug misuse over time and links with their mental health and well-being

Prescription drug misuse (PDM) is a mounting public health concern in the U.S., particularly among college students. The field's reliance on cross-sectional designs and limited controls for other substance use has failed to capture the specific role of misuse for longer-term health. Therefore, the present study tested associations between trajectories of PDM problems and college students' mental health and subjective happiness over time. Participants were 300 students who completed a baseline assessment (T1) and follow-ups every 6 months for two years (T2-T5). Participants self-reported problems associated with PDM and mental health. Results from univariate latent growth models indicated that problems with PDM were initially on an increasing trajectory. Based on parallel process models, problems with PDM were concurrently associated with higher levels of depressive symptoms, disinhibition, callousness/aggression, and lower levels of subjective happiness at T1. Further, we found support for parallel trajectories between PDM problems and both depressive symptoms and general disinhibition. Participants whose PDM problems were on an increasing (worsening) trajectory at baseline showed an increase in depressive symptoms and general disinhibition over the next two years. Participants whose PDM problems accelerated over time (got worse at a faster and faster rate over time) also showed a significant increase in their depressive symptoms over time. Most findings, however, were no longer statistically significant in sensitivity analyses that controlled for alcohol and other drug problems. Findings highlight college as an important time for interventions to prevent substance use and its associated negative consequence on later young adult mental health.

Different Effects of Total Bilirubin on 90-Day Mortality in Hospitalized Patients With Cirrhosis and Advanced Fibrosis: A Quantitative Analysis

Introduction: Total bilirubin (TB) is a major prognosis predictor representing liver failure in patients with acute on chronic liver failure (ACLF). However, the cutoff value of TB for liver failure and whether the same cutoff could be applied in both cirrhotic and non-cirrhotic patients remain controversial. There is a need to obtain the quantitative correlation between TB and short-term mortality via evidence-based methods, which is critical in establishing solid ACLF diagnostic criteria.

Methods: Patients hospitalized with cirrhosis or advanced fibrosis (FIB-4 > 1.45) were studied. TB and other variables were measured at baseline. The primary outcome was 90-day transplantation-free mortality. Multi-variable Cox proportional hazard model was used to present the independent risk of mortality due to TB. Generalized additive model and second derivate (acceleration) were used to plot the “TB-mortality correlation curves.” The mathematical (maximum acceleration) and clinical (adjusted 28-day transplantation-free mortality rate reaching 15%) TB cutoffs for liver failure were both calculated.

Results: Among the 3,532 included patients, the number of patients with cirrhosis and advanced fibrosis were 2,592 and 940, respectively, of which cumulative 90-day mortality were 16.6% (430/2592) and 7.4% (70/940), respectively. Any increase of TB was found the independent risk factor of mortality in cirrhotic patients, while only TB >12 mg/dL independently increased the risk of mortality in patients with advanced fibrosis. In cirrhotic patients, the mathematical TB cutoff for liver failure is 14.2 mg/dL, with 23.3% (605/2592) patients exceeding it, corresponding to 13.3 and 25.0% adjusted 28- and 90-day mortality rate, respectively. The clinical TB cutoff for is 18.1 mg/dL, with 18.2% (471/2592) patients exceeding it. In patients with advanced fibrosis, the mathematical TB cutoff is 12.1 mg/dL, 33.1% (311/940) patients exceeding it, corresponding to 2.9 and 8.0% adjusted 28- and 90-day mortality rate, respectively; the clinical TB cutoff was 36.0 mg/dL, 1.3% (12/940) patients above it.

Conclusion: This study clearly demonstrated the significantly different impact of TB on 90-day mortality in patients with cirrhosis and advanced fibrosis, proving that liver failure can be determined by TB alone in cirrhosis but not in advanced fibrosis. The proposed TB cutoffs for liver failure provides solid support for the establishment of ACLF diagnostic criteria.

Delineating Developmental Periods in Adulthood Suggests Age-Related Shifts in the Correlates of Alcohol Use and Problems

BACKGROUND

Developmental context is related to the propensity to engage in alcohol use, the rate at which alcohol use changes, and the relevance of different risk factors to alcohol use disorder (AUD). Therefore, studies of change should consider developmental nuances, but change is often modeled to follow a uniform pattern, even across distinct developmental periods.

METHODS

This study implemented a novel analytic approach to delineate developmental periods of alcohol behavior (n = 478, ages 18 to 35). This approach was further leveraged to examine age-related shifts in the association of impulsivity risk factors (lack of planning, general sensation seeking, alcohol enhancement expectancies) with alcohol behavior (alcohol quantity*frequency, heavy drinking, AUD).

RESULTS

A sequence of exploratory and confirmatory latent growth models (LGMs) suggested modeling separate linear change factors for alcohol behavior during the primary college (ages 18 to 21) and postcollege years (21 to 35). Bivariate LGMs estimated correlations for alcohol behavior changes with lack of planning, sensation seeking, and enhancement expectancies during these periods. The rate at which heavy drinking changed during the college years was positively correlated with general sensation seeking and lack of planning during this period (rs = 0.61 to 0.63). These correlations were significantly weaker during the postcollege years (rs = 0.29 to 0.34). Notably, the rate of change in alcohol behavior was strongly correlated with enhancement expectancies during the college (r = 0.45 to 0.70) and postcollege years (r = 0.45 to 0.61).

CONCLUSIONS

These findings highlight the importance of sensation seeking and lack of planning with regard to adult alcohol use, particularly in a college environment. There was also a strong link between the rates of change in alcohol behavior and enhancement expectancies across all waves. This study supports the utility of exploratory LGMs for delineating developmental periods of alcohol behavior, which are characterized by different processes.

Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview

Time series analysis of intensive longitudinal data provides the psychological literature with a powerful tool for assessing how psychological processes evolve through time. Recent applications in the field of psychosomatic research have provided insights into the dynamical nature of the relationship between somatic symptoms, physiological measures, and emotional states. These promising results highlight the intrinsic value of employing time series analysis, although application comes with some important challenges. This paper aims to present an approachable, non-technical overview of the state of the art on these challenges and the solutions that have been proposed, with emphasis on application towards psychosomatic hypotheses. Specifically, we elaborate on issues related to measurement intervals, the number and nature of the variables used in the analysis, modeling stable and changing processes, concurrent relationships, and extending time series analysis to incorporate the data of multiple individuals. We also briefly discuss some general modeling issues, such as lag-specification, sample size and time series length, and the role of measurement errors. We hope to arm applied researchers with an overview from which to select appropriate techniques from the ever growing variety of time series analysis approaches.

Empirical Bayes Derivative Estimates

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25-500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.

Modeling dynamic processes with panel data: An application of continuous time models to prevention research

Many interventions are characterized by repeated observations on the same individuals (e.g., baseline, mid-intervention, two to three post-intervention observations), which offer the opportunity to consider differences in how individuals vary over time. Effective interventions may not be limited to changing means, but instead may also include changes to how variables affect each other over time. Continuous time models offer the opportunity to specify differing underlying processes for how individuals change from one time to the next, such as whether it is the level or change in a variable that is related to changes in an outcome of interest. After introducing continuous time models, we show how different processes can produce different expected covariance matrices. Thus, models representing differing underlying processes can be compared, even with a relatively small number of repeated observations. A substantive example comparing models that imply different underlying continuous time processes will be fit using panel data, with parameters reflecting differences in dynamics between control and intervention groups.

VIII. THE PAST, PRESENT, AND FUTURE OF DEVELOPMENTAL METHODOLOGY

This chapter selectively reviews the evolution of quantitative practices in the field of developmental methodology. The chapter begins with an overview of the past in developmental methodology, discussing the implementation and dissemination of latent variable modeling and, in particular, longitudinal structural equation modeling. It then turns to the present state of developmental methodology, highlighting current methodological advances in the field. Additionally, this section summarizes ample quantitative resources, ranging from key quantitative methods journal articles to the various quantitative methods training programs and institutes. The chapter concludes with the future of developmental methodology and puts forth seven future innovations in the field. The innovations discussed span the topics of measurement, modeling, temporal design, and planned missing data designs. Lastly, the chapter closes with a brief overview of advanced modeling techniques such as continuous time models, state space models, and the application of Bayesian estimation in the field of developmental methodology.