Andrea Lamont

Initiation and Persistence of Alcohol Use in United States Black, Hispanic, and White Male and Female Youth

BACKGROUND The relation between early and frequent alcohol use and later difficulties is quite strong. However, the degree that alcohol use persists, which is often a necessary cause for developing alcohol-related problems or an alcohol use disorder, is not well studied, particularly with attention to race and gender. A novel statistical approach, the Multi-facet Longitudinal Model, enables the concurrent study of age of initiation and persistence. METHODS The models were applied to longitudinal data on youth alcohol use from ages 12 through 19, collected in the (U.S.) National Longitudinal Survey of Youth 1997 cohort (N=8984). RESULTS Results confirmed that Black adolescents initiate alcohol use at later ages than do White youth. Further, after initiation, White adolescents were substantially more likely than Black adolescents to continue reporting alcohol use in subsequent years. Hispanic teens showed an intermediate pattern. Gender differences were more ambiguous, with a tendency for boys to be less likely to continue drinking after initiation than were girls. CONCLUSIONS Novel findings from the new analytic models suggest differential implications of early alcohol use by race and gender. Early use of alcohol might be less consequential for males who initiate alcohol use early, Black, and Hispanic youth than for their female and White counterparts.

Predicting high-risk versus higher-risk substance use during late adolescence from early adolescent risk factors using Latent Class Analysis.

Much of the existing risk factor literature focuses on identifying predictors of low levels of substance use versus higher-levels of substance use. In this article, we explore more nuanced patterns of alcohol, tobacco and other drug (ATOD) use during late adolescence. Our aims were to: (1) identify subgroups of youth with qualitatively different patterns of ATOD use and (2) explore whether membership among qualitatively distinct, high-risk classes could be predicted based on early adolescent risk factors. Data came from a selected subsample of the National Longitudinal Survey of Youth (n = 1689). Predictors were measured when youth were about 12 years old; ATOD use was assessed when youth were aged 17 years. Results showed that adolescent ATOD use is not a homogenous behavior. Four distinct classes of adolescent ATOD users were derived. Each class had a qualitatively distinct and discriminable pattern of ATOD use. Ecological predictors were shown to differentiate between latent classes, with peer factors playing a particularly important role in differentiating between high-risk and higher-risk users. Implications for prevention and limitations are discussed.

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.

Finite Mixtures for Simultaneously Modelling Differential Effects and Non-Normal Distributions

Regression mixture models have been increasingly applied in the social and behavioral sciences as a method for identifying differential effects of predictors on outcomes. Although the typical specification of this approach is sensitive to violations of distributional assumptions, alternative methods for capturing the number of differential effects have been shown to be robust. Yet, there is still a need to better describe differential effects that exist when using regression mixture models. This study tests a new approach that uses sets of classes (called differential effects sets) to simultaneously model differential effects and account for nonnormal error distributions. Monte Carlo simulations are used to examine the performance of the approach. The number of classes needed to represent departures from normality is shown to be dependent on the degree of skew. The use of differential effects sets reduced bias in parameter estimates. Applied analyses demonstrated the implementation of the approach for describing differential effects of parental health problems on adolescent body mass index using differential effects sets approach. Findings support the usefulness of the approach, which overcomes the limitations of previous approaches for handling nonnormal errors.

Identification of predicted individual treatment effects in randomized clinical trials

In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.

Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Abstract Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.

Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures.

Structural Equation Modeling: Applications in Mplus

Since its development, Mplus (Muthén & Muthén, 1998–2012) has become the software of choice for many users of latent variable models. The development of a unified approach to a general latent variable modeling framework, computational efficiency, the rapid addition of new methods and models, and the relative ease of programming are some of the features that have made Mplus very popular. This book review is for Wang and Wang’s recent book, Structural Equation Modeling: Applications in Mplus. The book provides an overview of structural equation modeling, as well as detailed instruction of how to specify commonly fit structural equation models with the Mplus software. The first chapter of this book briefly introduces structural equation modeling through five steps, which follow Bollen and Long (1993): (a) model formulation, (b) model identification, (c) model estimation, (d) model evaluation, and (e) model modification. The following chapters each cover one of the most commonly used structural equation models, such as measurement modeling (confirmatory factor analysis [CFA] in Chapter 2), latent growth modeling (LGM in Chapter 4), multigroup modeling (Chapter 5), and mixture modeling (Chapter 6).

Is ADHD, independent of ODD, associated with whether and why college students misuse stimulant medication?

Although previous research suggests that undergraduates with untreated or undertreated attention-deficit/hyperactivity disorder (ADHD) symptoms may have academic motives for stimulant medication misuse, no previous work has examined the relation of ADHD symptoms, controlling for comorbid oppositional defiant disorder (ODD), to misuse, or has explored how these symptoms are differentially related to motives for misuse. Among a sample of 900 students from one public university, the current study first tested whether increased ADHD symptomology (using the Current Symptoms Scale, CSS) was associated with an increased likelihood of misusing stimulant medication, controlling for comorbid ODD. We then examined whether those with increased ADHD symptomology were more likely to report academic motives for misuse. The prevalence rate of misuse in the past year was 22%. Participants who met symptom count criteria for ADHD (controlling for comorbid ODD) were 2.90 times more likely to misuse stimulant medication than those who did not. Among misusers, those who met ADHD criteria were also 2.80 times more likely to report academic motives for misuse. These results support that stimulant medication misuse is likely driven, in part, by inadequate or absent care for the executive functioning impairments associated with ADHD. Therefore, a greater focus on assessment and treatment of college students with ADHD symptoms is warranted.