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Introduction: Blunt and hemp wraps, as a means of consuming cannabis, have emerged into the retail space where the prevalence has been increasing since 2017. There is limited epidemiological research on the prevalence of use of these products across the U.S. particularly among young adults who are at greater risk of tobacco and cannabis use. Methods: This study draws from a U.S. national representative sample of young adults (n = 1178) captured in May 2022. Respondents participated in an online survey about their use of blunt and hemp wraps. Multinomial regression was used to examine differences in sociodemographic characteristics (gender, race/ethnicity, sexual orientation, educational attainment, and region) in relation to use of each wrap type. Results: One quarter (22.7%) of young adults reported ever having used a blunt wrap, 3.2% in the past 30-days. One in seven (14.3%) had ever used a hemp wrap, 2.3% in the past 30-days. Non-Hispanic Black young adults were 1.55 and 2.91 times as likely to have ever used blunt or hemp wraps, respectively, compared to non-Hispanic Whites. Similarly, participants who identified as gay or lesbian or bisexual similarly had greater odds of having ever used blunt or hemp wraps. Hispanic young adults were 2.49 times as likely to have used hempwraps compared to non-Hispanic Whites. Conclusion: Blunt and hemp wrap use is prevalent among young adults, particularly among minoritized populations. Continued research and surveillance of use of these products is needed to fully evaluate the impact their use may have on the broader population. [ABSTRACT FROM AUTHOR]

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data. [ABSTRACT FROM AUTHOR]

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing an improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through the implementation of moderated non-linear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex model building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided. [ABSTRACT FROM AUTHOR]

Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable—distal outcomes—providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students' mathematics attitudes, linking latent class membership to two distal outcomes. [ABSTRACT FROM AUTHOR]

The use of latent class analysis, and finite mixture modeling more generally, has become almost commonplace in social and health science domains. Typically, research aims in mixture model applications include investigating predictors and distal outcomes of latent class membership. The most recent developments for incorporating latent class antecedents and consequences are stepwise procedures that decouple the classification and prediction models. It was initially believed these procedures might avoid the potential misspecification bias in the simultaneous models that include both latent class indicators and predictors. However, if direct effects from the predictors to the indicators are omitted in the stepwise procedure, the prediction model can yield biased estimates. This article presents a logical and principled approach, readily implemented in current software, to testing for direct effects from latent class predictors to indicators using multiple indicator multiple cause modeling. This approach is illustrated with real data and opportunities for future developments are discussed. [ABSTRACT FROM PUBLISHER]

For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates. [ABSTRACT FROM PUBLISHER]

Quantitative research on parental/family disapproval and rejection of sexual/gender minority persons has often measured family rejection as one binary/continuous variable, or using several variables representing specific behaviors or dimensions of behaviors. Absent from this literature is analysis using a person-oriented approach, examining heterogeneity across individuals in the types of family treatment experience. Using data from 2,664 adult sexual minority women and transmen in Vietnam, latent class analysis was conducted on 19 items representing negative family behaviors. The six-class solution best fit the data, including one non-negative class (peace, 36.7% of the sample) and five negative classes (pressure, 34.0%;aggressive to respondent and girlfriend, 10.3%;aggressive to respondent, 8.1%;severe, 6.0%; andextreme, 4.7%). Class membership was regressed on individual, family, and contextual variables. Overall, younger age, transman identity, religious affiliation, and parent awareness predicted being in worse family treatment classes. Further research is needed to separate cohort and age effects and to examine developmental trajectories of family behavior. Findings suggested that it may be general conservativeness rather than a specific religious doctrine that predicts negative family treatment and revealed that nonparent family members’ role in family response to sexual/gender nonconformity may be significant. [ABSTRACT FROM PUBLISHER]

Individuals with autism demonstrate atypical and variable responses to social and emotional stimuli, perhaps reflecting heterogeneity of the disorder. The goal of this study was to determine whether unique profiles of psychophysiological responses to such stimuli could be identified in individuals diagnosed with autism spectrum disorder (ASD), with fragile X syndrome (FXS), and with comorbid autism and fragile X syndrome (ASD + FXS), and in typically developing (TYP) individuals. This study included 52 boys (ages 10–17): idiopathic ASD (n = 12), FXS (n = 12), comorbid ASD + FXS (n = 17), and TYP (n = 11). Physiological responses, including potentiated startle, electrodermal response, heart rate variability, and vagal tone, were collected concurrently while participants viewed emotionally evocative pictures of human faces or nonsocial images. Although some of these measures have been utilized separately for investigations on these diagnostic groups, they have not been considered together. Results using Kruskal-Wallis one-way analysis of variance by ranks indicate statistically significant differences in distributions of autonomic regulation responses between groups. The most notable differences were between the ASD group and both the FXS groups on measures of sympathetic activity, with FXS groups evincing increased activity. Also, both the ASD and ASD + FXS groups showed significantly decreased parasympathetic activity compared with FXS and TYP groups. In addition, the ASD + FXS group demonstrated a unique distribution of startle potentiation and arousal modulation. This study provides evidence that autonomic arousal and regulation profiles could be useful for distinguishing subgroups of autism and shed light on the variability underlying emotional responsivity. [ABSTRACT FROM AUTHOR]

Mild to moderate skew in errors can substantially impact regression mixture model results; one approach for overcoming this includes transforming the outcome into an ordered categorical variable and using a polytomous regression mixture model. This is effective for retaining differential effects in the population; however, bias in parameter estimates and model fit warrant further examination of this approach at higher levels of skew. The current study used Monte Carlo simulations; 3000 observations were drawn from each of two subpopulations differing in the effect ofXonY. Five hundred simulations were performed in each of the 10 scenarios varying in levels of skew in one or both classes. Model comparison criteria supported the accurate two-class model, preserving the differential effects, while parameter estimates were notably biased. The appropriate number of effects can be captured with this approach but we suggest caution when interpreting the magnitude of the effects. [ABSTRACT FROM AUTHOR]

Regression mixture models, which have only recently begun to be used in applied research, are a new approach for finding differential effects. This approach comes at the cost of the assumption that error terms are normally distributed within classes. This study uses Monte Carlo simulations to explore the effects of relatively minor violations of this assumption. The use of an ordered polytomous outcome is then examined as an alternative that makes somewhat weaker assumptions, and finally both approaches are demonstrated with an applied example looking at differences in the effects of family management on the highly skewed outcome of drug use. Results show that violating the assumption of normal errors results in systematic bias in both latent class enumeration and parameter estimates. Additional classes that reflect violations of distributional assumptions are found. Under some conditions it is possible to come to conclusions that are consistent with the effects in the population, but when errors are skewed in both classes the results typically no longer reflect even the pattern of effects in the population. The polytomous regression model performs better under all scenarios examined and comes to reasonable results with the highly skewed outcome in the applied example. We recommend that careful evaluation of model sensitivity to distributional assumptions be the norm when conducting regression mixture models. [ABSTRACT FROM PUBLISHER]

The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that 'softer' drugs lead to 'harder,' illicit drugs. However, the gateway hypothesis has been notably difficult to directly test-that is, to test as competing hypotheses in a single model that licit drug use might lead to illicit drug use or the reverse. This article presents a novel statistical technique, dual-process discrete-time survival analysis, which enables this comparison. This method uses mixture-modeling software to estimate 2 concurrent time-to-event processes and their effects on each other. Using this method, support for the gateway hypothesis in the National Longitudinal Survey of Youth, 1997, was weak. However, this article was not designed as a strong test of causal direction but more as a technical demonstration and suffered from certain technological limitations. Both these limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]