M. Lee Van Horn

Modeling predictors of latent classes in regression mixture models

The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed.The purpose of the current study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that the step-1 of the three-step approach shows adequate results in class enumeration, we suggest using an alternative approach: 1) decide the number of latent classes without predictors of latent classes and 2) bring the latent class predictors into the model with the inclusion of hypothesized direct covariates effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students academic achievement outcome. Implications of the study are discussed.

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.

Longitudinal relationships between self-concept for physical activity and neighborhood social life as predictors of physical activity among older African American adults

BackgroundEngaging in regular physical activity (PA) as an older adult has been associated with numerous physical and mental health benefits. The aim of this study is to directly compare how individual-level cognitive factors (self-efficacy for PA, self-determined motivation for PA, self-concept for PA) and neighborhood perceptions of the social factors (neighborhood satisfaction, neighborhood social life) impact moderate-to-vigorous physical activity (MVPA) longitudinally among older African American adults.MethodsData were analyzed from a sub-set of older African American adults (Nxa0=xa0224, Magexa0=xa063.23xa0years, SDxa0=xa08.74, 63.23% female, MBody Mass Indexxa0=xa032.01, SDxa0=xa07.52) enrolled in the Positive Action for Today’s Health trial. MVPA was assessed using 7-day accelerometry-estimates and psychosocial data (self-efficacy for PA, self-determined motivation for PA, self-concept for PA, neighborhood satisfaction, neighborhood social life) were collected at baseline, 12-, 18-, and 24-months.ResultsMultilevel growth modeling was used to examine within- and between-person effects of individual-level cognitive and social environmental factors on MVPA. At the between-person level, self-concept (bxa0=xa00.872, SExa0=xa00.239, pxa0<xa00.001), and neighborhood social life (bxa0=xa00.826, SExa0=xa00.176, pxa0<xa00.001) predicted greater MVPA, whereas neighborhood satisfaction predicted lower MVPA (bxa0=xa0−0.422, SExa0=xa00.172, pxa0=xa00.015). Among the between-person effects, only average social life was moderated by time (bxa0=xa00.361, SExa0=xa00.147, pxa0=xa00.014), indicating that the impact of a relatively positive social life on MVPA increased across time. At the within-person level, positive increases in self-concept (bxa0=xa00.294, SExa0=xa00.145, pxa0=xa00.043) and neighborhood social life (bxa0=xa00.270, SExa0=xa00.113, pxa0=xa00.017) were associated with increased MVPA.ConclusionsThese results suggest that people with a higher average self-concept for PA and a more positive social life engaged in greater average MVPA. Additionally, changes in perceptions of one’s neighborhood social life and one’s self-concept for PA were associated with greater MVPA over 2xa0years. These factors may be particularly relevant for future interventions targeting long-term change and maintenance of MVPA in older African Americans.Trial registrationClinicalTrials.Gov #NCT01025726 registered 1 December 2009.

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.

Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

Latent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that confounds indirect effects. One way to address this untestable assumption is to conduct sensitivity analysis to assess whether the inference about an indirect effect would change under varying degrees of confounding bias. We developed a sensitivity analysis technique for a latent growth curve mediation model. We compute the biasing effect of confounding on point and confidence interval estimates of the indirect effects in a structural equation modeling framework. We illustrate sensitivity plots to visualize the effects of confounding on each indirect effect and present an empirical example to illustrate the application of the sensitivity analysis.

The (Lack of) Replication of Self-Reported Mindfulness as a Mechanism of Change in Mindfulness-Based Relapse Prevention for Substance Use Disorders

The development and evaluation of mindfulness-based interventions for a variety of psychological and medical disorders have grown exponentially over the past 20xa0years. Yet, calls for increasing the rigor of mindfulness research and recognition of the difficulties of conducting research on the topic of mindfulness have also increased. One of the major difficulties is the measurement of mindfulness, with varying definitions across studies and ambiguity with respect to the meaning of mindfulness. There is also concern about the reproducibility of findings given few attempts at replication. The current secondary analysis addressed the issue of reproducibility and robustness of the construct of self-reported mindfulness across two separate randomized clinical trials of mindfulness-based relapse prevention (MBRP), as an aftercare treatment for substance use disorder. Specifically, we tested the robustness of our previously published findings, which identified a latent construct of mindfulness as a significant mediator of the effect of MBRP on reducing craving following treatment. First, we attempted to replicate the findings in a separate randomized clinical trial of MBRP. Second, we conducted sensitivity analyses to test the assumption of the no-omitted confounder bias in a mediation model. The effect of MBRP on self-reported mindfulness and overall mediation effect failed to replicate in a new sample. The effect of self-reported mindfulness in predicting craving following treatment did replicate and was robust to the no-omitted confounder bias. The results of this work shine a light on the difficulties in the measurement of mindfulness and the importance of examining the robustness of findings.

Does the structure of working memory in EL children vary across age and two language systems

ABSTRACT This study examined the cross-sectional structure of working memory (WM) among elementary school English learners (ELs). A battery of WM tasks was administered in Spanish (L1) and English (L2) within five age groups (ages 6, 7, 8, 9, & 10). Confirmatory factor analysis showed a three-factor structure of WM emerged in both L1 and L2 administrations for each age group. The important findings, however, were: (1) the separation between the executive component and storage component (phonological loop) structure of WM increased as a function of age within both language systems, (2) the structure of WM supported a domain general phonological storage component and a domain general executive system across both language systems, and (3) the visual-spatial WM system shared minimal variance with the executive system. Taken together, the findings support Baddeley’s multicomponent model (e.g., Baddeley & Logie, 1999. The multiple-component model. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 28–61). Cambridge: Cambridge University Press) as a good fit to the structure of WM in EL children’s English and Spanish language system.

Project SHINE: effects of a randomized family-based health promotion program on the physical activity of African American parents

This study examined the effects of a family-based health promotion intervention on the moderate-to-vigorous physical activity (MVPA), light physical activity, sedentary behavior, and fruit and vegetable intake of African American parents. Eighty-nine African American parents (41.5u2009±u20098.5xa0years; 92% females; 74% obese; 64%u2009<u2009

The Effects of Sample Size on the Estimation of Regression Mixture Models

40xa0K income) and adolescents (12.5u2009±u20091.4xa0years; 61% girls; 48% obese) were randomized to a 6-week behavioral skills plus positive parenting and peer monitoring intervention grounded in social cognitive, self-determination, and family systems theories or a general health comparison program. Parents wore accelerometers for 7xa0days and completed three 24-h dietary recalls at baseline and post-intervention. Multilevel regression models (controlling for baseline variables) demonstrated a significantly greater increase in parent MVPA for those in the intervention versus comparison condition (bu2009=u20099.44, SEu2009=u20094.26, pu2009<u20090.05). There were no other significant effects. Family-based approaches that include African American parents and youth may increase parent MVPA and hold promise for preventing chronic diseases.