Multigenerational adversity impacts on human gut microbiome composition and socioemotional functioning in early childhood.

Adversity exposures in the prenatal and postnatal period are associated with an increased risk for psychopathology, which can be perpetuated across generations. Nonhuman animal research highlights the gut microbiome as a putative biological mechanism underlying such generational risks. In a sample of 450 mother-child dyads living in Singapore, we examined associations between three distinct adversity exposures experienced across two generations-maternal childhood maltreatment, maternal prenatal anxiety, and second-generation children's exposure to stressful life events-and the gut microbiome composition of second-generation children at 2 y of age. We found distinct differences in gut microbiome profiles linked to each adversity exposure, as well as some nonaffected microbiome features (e.g., beta diversity). Remarkably, some of the microbial taxa associated with concurrent and prospective child socioemotional functioning shared overlapping putative functions with those affected by adversity, suggesting that the intergenerational transmission of adversity may have a lasting impact on children's mental health via alterations to gut microbiome functions. Our findings open up a new avenue of research into the underlying mechanisms of intergenerational transmission of mental health risks and the potential of the gut microbiome as a target for intervention.

Exploration of the MCMC Wald test with linear regression.

Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal, 28, 1-14, (2021a, 2021b) proposed a variant of the Wald test that uses Markov chain Monte Carlo machinery to generate a chi-square test statistic for frequentist inference. Because the test's composition does not rely on analytic expressions for sampling variation and covariation, it potentially provides a way to get honest significance tests in cases where the likelihood-based test statistic's assumptions break down (e.g., in small samples). The goal of this study is to use simulation to compare the new MCM Wald test to its maximum likelihood counterparts, with respect to both their type I error rate and power. Our simulation examined the test statistics across different levels of sample size, effect size, and degrees of freedom (test complexity). An additional goal was to assess the robustness of the MCMC Wald test with nonnormal data. The simulation results uniformly demonstrated that the MCMC Wald test was superior to the maximum likelihood test statistic, especially with small samples (e.g., sample sizes less than 150) and complex models (e.g., models with five or more predictors). This conclusion held for nonnormal data as well. Lastly, we provide a brief application to a real data example.

Early-life adversity and risk for depression and anxiety: The role of interpersonal support.

Early-life adversity is a major risk factor for psychopathology, but not all who experience adversity develop psychopathology. The current study evaluated whether the links between child and adolescent adversity and depression and anxiety were described by general benefits and/or buffering effects of interpersonal support. Data from 456 adolescents oversampled on neuroticism over a 5-year period were examined in a series of discrete-time survival analyses to predict subsequent disorder onsets. Models examined linear, quadratic, and interactive effects of interpersonal support over time, as measured by chronic interpersonal stress interview ratings. Results did not support buffering effects of interpersonal support against either child or adolescent adversity in predicting depression or anxiety. However, there was support for the general benefits model of interpersonal support as evidenced by follow-up analyses of significant quadratic effects of interpersonal support, demonstrating that higher interpersonal support led to decreased likelihood of depression and anxiety onsets. Secondary analyses demonstrated that effects of interpersonal support remained after accounting for baseline depression and anxiety diagnoses. Further, quadratic effects were driven by social domains as opposed to familial domains when considering child adversity. Implications for interventions and randomized controlled prevention trials regarding interpersonal relationships are discussed.

An Investigation of Factored Regression Missing Data Methods for Multilevel Models with Cross-Level Interactions.

A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study presents a series of Monte Carlo computer simulations that investigates Bayesian and multiple imputation strategies based on factored regressions. When the model's distributional assumptions are satisfied, these methods generally produce nearly unbiased estimates and good coverage, with few exceptions. Severe misspecifications that arise from substantially non-normal distributions can introduce biased estimates and poor coverage. Follow-up simulations suggest that a Yeo-Johnson transformation can mitigate these biases. A real data example illustrates the methodology, and the paper suggests several avenues for future research.

Comparing DIC and WAIC for multilevel models with missing data.

In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). We use a multilevel mediation model as an illustrative example to compare different types of DIC and WAIC. More specifically, we aim to compare the performance of conditional and marginal DICs and WAICs, and investigate their performance with missing data. We focus on two versions of DIC ([Formula: see text] and [Formula: see text]) and one version of WAIC. In addition, we explore whether it is necessary to include the nuisance models of incomplete exogenous variables in likelihood. Based on the simulation results, whether [Formula: see text] is better than [Formula: see text] and WAIC and whether we should include the nuisance models of exogenous variables in likelihood functions depend on whether we use marginal or conditional likelihoods. Overall, we find that the marginal likelihood based-[Formula: see text] that excludes the likelihood of covariate models generally had the highest true model selection rates.

Combination treatment with varenicline and naltrexone reduces World Health Organization risk drinking levels.

BACKGROUND: The U.S. Food and Drug Administration identifies abstinence and the absence of heavy drinking days as outcomes for pharmacotherapy trials for alcohol use disorder (AUD). However, many individuals with AUD struggle to achieve these outcomes, which may discourage them from seeking treatment. World Health Organization (WHO) risk drinking levels have garnered attention in the alcohol field as potential non-abstinent outcomes for AUD medication trials. Further, testing combination pharmacotherapy for AUD represents an important direction in the field, particularly using medications such as naltrexone and varenicline, which are approved for treating AUD and smoking, respectively. The objective of the current study was to test the utility of the WHO risk drinking levels as a drinking outcome in a randomized clinical trial of combined varenicline and naltrexone for smoking cessation and drinking reduction. These analyses provide additional tests of the efficacy of this combination treatment. METHODS: The current study is a secondary analysis of a phase 2, randomized, double-blind clinical trial, wherein participants (N = 165) who were daily smokers and heavy drinkers were randomly assigned to receive either 2 mg/day of varenicline plus 50 mg/day of naltrexone or 2 mg/day of varenicline plus placebo for 12 weeks. Medication effects on 1- and 2-level reductions in WHO risk drinking levels were assessed at 4, 8, and 12 weeks into the active medication period. RESULTS: In logistic growth curve models individuals receiving the combined treatment had greater reductions in WHO risk drinking levels than individuals taking varenicline alone when assessed at 4 weeks into the active medication period. Among individuals who were WHO high and very high risk drinkers at baseline, the largest effect sizes favoring combination treatment were at Week 4 for the WHO 2-level reduction outcome (Cohen's h = 0.202) and Week 12 for the WHO 1-level reduction outcome (Cohen's h = 0.244), although these effects did not reach statistical significance. CONCLUSIONS: These findings provide evidence that combined varenicline plus naltrexone treatment is effective at reducing WHO risk drinking levels, particularly among individuals who smoke cigarettes daily and drink heavily. These results add to a growing body of literature validating reductions in WHO risk drinking levels as outcomes of alcohol medication trials.

A Bayesian Latent Variable Selection Model for Nonignorable Missingness.

Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method can handle missingness appropriately. Computer simulation results suggested that the proposed Bayesian latent variable selection model (BLVSM) was quite effective when the outcome and/or covariates were MNAR. Except when the sample size was small, estimates from the proposed BLVSM tracked closely with those from the complete data analysis. With a small sample size, when the outcome was less predictable from the covariates, the missingness proportions of the covariates and the outcome were larger, and the missingness selection processes of the covariates and the outcome were more MNAR and MAR, the performance of BLVSM was less satisfactory. When the sample size was large, BLVSM always performed well. In contrast, the method with an MAR assumption provided biased estimates and undercoverage confidence intervals when the missingness was MNAR. The robustness and the implementation of BLVSM in real data were also illustrated. The proposed method is available in the Blimp software application, and the paper includes a data analysis example illustrating its use.

The Effects of Middle School Weight Climate on Youth With Higher Body Weight.

This study examines whether social-emotional difficulties associated with higher body weight vary across schools as a function of the school's weight climate. Weight climate, characterized by weight-policing, was assessed indirectly by examining how strongly self-reported weight predicts victim reputation within 26 ethnically diverse middle schools. Social-emotional indicators included self-reported loneliness, school belonging, and self-esteem. In schools with stronger weight-policing at seventh grade, loneliness was intensified by eighth grade among both girls (n = 2,101) and boys (n = 1,985) with higher weight. Similar effects were found for low self-esteem among girls. Additionally, boys-regardless of their weight-reported lower sense of belonging in schools with stronger weight-policing. The study offers a new method to estimate school weight climate, and the findings provide insights for interventions.

A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance Matrices.

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for random coefficient analyses. This study is apparently the first to directly compare these procedures. Analytic results suggest that both imputation procedures can introduce bias-inducing incompatibilities with a random coefficient analysis model. Problems with fully conditional specification result from an incorrect distributional assumption, whereas joint imputation uses an underparameterized model that assumes uncorrelated intercepts and slopes. Monte Carlo simulations suggest that biases from these issues are tolerable if the missing data rate is 10% or lower and the sample is composed of at least 30 clusters with 15 observations per group. Furthermore, fully conditional specification tends to be superior with intraclass correlations that are typical of crosssectional data (e.g., ICC = .10), whereas the joint model is preferable with high values typical of longitudinal designs (e.g., ICC = .50).

Gender Norm Salience Across Middle Schools: Contextual Variations in Associations Between Gender Typicality and Socioemotional Distress.

Youth who feel they do not fit with gender norms frequently experience peer victimization and socioemotional distress. To gauge differences between schools, the current study examined the longitudinal effects of school-level gender norm salience-a within-school association between gender typicality and peer victimization-on socioemotional distress across 26 ethnically diverse middle schools (n boys = 2607; n girls = 2805). Boys (but not girls) reporting lower gender typicality experienced more loneliness and social anxiety in schools with more salient gender norms, even when accounting for both individual and school level victimization. Greater gender norm salience also predicted increased depressed mood among boys regardless of gender typicality. These findings suggest particular sensitivity among boys to environments in which low gender typicality is sanctioned.

Evaluation of Multi-parameter Test Statistics for Multiple Imputation.

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.

Centering Predictor Variables in Three-Level Contextual Models.

Hierarchical data are becoming increasingly complex, often involving more than two levels. Centering decisions in multilevel models are closely tied to substantive hypotheses and require researchers to be clear and cautious about their choices. This study investigated the implications of group mean centering (i.e., centering within context; CWC) and grand mean centering (CGM) of predictor variables in three-level contextual models. The goals were to (a) determine equivalencies in the means and variances across the centering options and (b) use the algebraic relationships between the centering choices to clarify the interpretation of the estimated parameters. We provide recommendations to assist the researcher in making centering decisions for analysis of three-level contextual models.

A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables.

This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naïvely rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.

Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation.

Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002 ; Graham, 2009 ; Enders, 2010 ). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.

A practice quit model to test early efficacy of medications for alcohol use disorder in a randomized clinical trial.

RATIONALE: Screening novel medications for alcohol use disorder (AUD) requires models that are both efficient and ecologically-valid. Ideally, such models would be associated with the outcomes of a given medication in clinical trials. OBJECTIVES: To test a novel human laboratory model in which individuals with intrinsic motivation to change their drinking engage in a "practice quit" attempt consisting of 6 days of complete abstinence from alcohol. METHOD: Individuals with current AUD completed a randomized, double-blind, placebo-controlled study of naltrexone (50 mg), varenicline (2 mg bid), or matched placebo. Participants were titrated onto the study medication for 1 week prior to starting the 6-day practice quit attempt. During the practice quit attempt, participants completed daily interviews with research staff. All participants completed an alcohol cue-exposure paradigm before starting the study medication and after 2 weeks of study medication. RESULTS: There were no significant medication effect on drinks per drinking day (F(2,49) = 0.66, p = 0.52) or percent days abstinent (F(2,49) = 0.14, p = 0.87) during the 6-day practice quit period. There were no medication effects on alcohol cue-reactivity (F(2,44) = 0.80, p = 0.46). Notably, participants sharply reduced their drinking during the entire 13-day medication treatment period, as compared to reducing only during the 6-day practice quit period. During the total medication period, higher levels of motivation to change was associated with higher percent days abstinent (F(1,49) = 8.12, p < 0.01). CONCLUSIONS: This study reports mostly null findings, which challenges us to decompose its nuanced design to consider model refinements. Possible changes to the model include considering the requirement for intrinsic motivation for change, including a longer practice quit period, encompassing the medication administration timeframe in the practice quit period, increasing the required sample size for signal detection, and examining a post COVID-19 pandemic cohort.

Longitudinal associations between adolescent skin color satisfaction and adult health outcomes in Black women.

OBJECTIVE: Although emerging studies examine the inverse relationship between body satisfaction and disordered eating for Black women, it has not been established how racially salient aspects of body satisfaction may have implications for eating behaviors and longitudinal health outcomes. METHOD: In a longitudinal sample of 455 Black women, we examined whether skin color satisfaction across ages 10-15 was directly related to adult health outcomes at age 40 (e.g., disordered eating, self-esteem, self-reported health, depressive symptoms, and cardiovascular risk). We also investigated the indirect impact of skin color satisfaction on adult health, mediated by body satisfaction, and binge eating. RESULTS: No significant direct or indirect effects of adolescent skin color satisfaction were observed for depressive symptoms or cardiovascular health outcomes. At ages 10 and 12, skin color satisfaction had negative and positive direct effects, respectively, on self-esteem. At age 15, greater skin color satisfaction was directly associated with greater self-reported health. Post hoc analyses revealed that when additionally accounting for adolescent body satisfaction, greater skin color satisfaction was indirectly associated with greater self-esteem and self-reported health, alongside lower cardiovascular risk. CONCLUSIONS: Although previous research suggests that in adolescence, Black girls' skin color satisfaction affects both body satisfaction and disordered eating behaviors, this association does not hold into midlife. Rather, post hoc analyses suggest that the lasting effects of adolescent skin color satisfaction are mediated by the longitudinal stability of body satisfaction, which in turn, is associated with adult health outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prenatal expectations in Mexican American women: development of a culturally sensitive measure.

Prenatal expectations describe various domains a woman envisions in preparation for her role as a new mother and influence how women transition into the maternal role. Although the maternal role is strongly influenced by the prevailing familial and sociocultural context, research characterizing prenatal expectations in ethnic minority and low-income women is lacking. As part of the largest growing minority group in the USA, Latina mothers represent an important group to study. Two hundred and ten low-income Mexican American women were administered the Prenatal Experiences Scale for Mexican Americans (PESMA) that was adapted to capture specific cultural aspects of prenatal expectations. Measures of current support, prenatal depressive symptoms, and other sociodemographic characteristics were also completed to assess validity. Exploratory factor analysis identified three underlying factors of prenatal expectations: paternal support, family support, and maternal role fulfillment. Associations among these subscales and demographic and cultural variables were conducted to characterize women who reported higher and lower levels of expectations. The PESMA demonstrated good concurrent validity when compared to measures of social support, prenatal depressive symptoms, and other sociodemographic constructs. A culturally sensitive measure of prenatal expectations is an important step towards a better understanding of how Mexican American women transition to the maternal role and identify culturally specific targets for interventions to promote maternal health.

A Bayesian Approach for Estimating Mediation Effects with Missing Data.

Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian missing data handling approach that can accommodate mediation analyses with any number of manifest variables. Computer simulation studies showed that the Bayesian approach produced frequentist coverage rates and power estimates that were comparable to those of maximum likelihood with the bias-corrected bootstrap. We share a SAS macro that implements Bayesian estimation and use two data analysis examples to demonstrate its use.

Missing data: An update on the state of the art.

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods. Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research. The paper addresses topics described in the original paper, including developments related to missing data theory, full information maximum likelihood, Bayesian estimation, multiple imputation, and models for missing not at random processes. The paper also describes newer factored regression specifications and missing data handling for multilevel models, both of which have been a focus of recent research. The paper concludes with a summary of the current software landscape and a discussion of several practical issues. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A simple Monte Carlo method for estimating power in multilevel designs.

Estimating power for multilevel models is complex because there are many moving parts, several sources of variation to consider, and unique sample sizes at Level 1 and Level 2. Monte Carlo computer simulation is a flexible tool that has received considerable attention in the literature. However, much of the work to date has focused on very simple models with one predictor at each level and one cross-level interaction effect, and approaches that do not share this limitation require users to specify a large set of population parameters. The goal of this tutorial is to describe a flexible Monte Carlo approach that accommodates a broad class of multilevel regression models with continuous outcomes. Our tutorial makes three important contributions. First, it allows any number of within-cluster effects, between-cluster effects, covariate effects at either level, cross-level interactions, and random coefficients. Moreover, we do not assume orthogonal effects, and predictors can correlate at either level. Second, our approach accommodates models with multiple interaction effects, and it does so with exact expressions for the variances and covariances of product random variables. Finally, our strategy for deriving hypothetical population parameters does not require pilot or comparable data. Instead, we use intuitive variance-explained effect size expressions to reverse-engineer solutions for the regression coefficients and variance components. We describe a new R package mlmpower that computes these solutions and automates the process of generating artificial data sets and summarizing the simulation results. The online supplemental materials provide detailed vignettes that annotate the R scripts and resulting output. (PsycInfo Database Record (c) 2023 APA, all rights reserved).