Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.

In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.

Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model.

Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT × COV. In detecting TRT × COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT × COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood, 2021). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT × COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.

Discretizing continuous variables in nutrition and obesity research: a practice that needs to be cut short.

BACKGROUND/OBJECTIVES: Nutrition and obesity researchers often dichotomize or discretize continuous independent variables to conduct an analysis of variance to examine group differences. We describe consequences associated with dichotomizing and discretizing continuous variables using two cross-sectional studies related to nutrition. SUBJECTS/METHODS: Study 1 investigated the effects of health literacy and nutrition knowledge on nutrition label accuracy (n = 612). Study 2 investigated the effects of cognitive restraint and BMI on fruit and vegetable (F/V) intake (n = 586). We compare analytic approaches where continuous independent variables were either discretized/dichotomized or analyzed as continuous variables. RESULTS: In Study 1, dichotomization of health literacy and nutrition knowledge for 2 × 2 ANOVA revealed health literacy had an effect on nutrition label accuracy. Nutrition knowledge has an effect on nutrition label accuracy, but the health literacy by nutrition knowledge interaction was not significant. When analyzed using regression, the nutrition knowledge effect was significant. The simple effect of health literacy was also significant when health literacy equals zero. Finally, the quadratic effect of health literacy was negative and significant. In Study 2, dichotomization and discretization of cognitive restraint and BMI were used for three ANOVAs, which discretized BMI in three ways. For all ANOVAs, the BMI main effect for predicting fruit and vegetable intake was significant, the interaction between BMI and cognitive restraint was non-significant, and cognitive restraint was only significant when both variables were dichotomized. When analyzed using regression, the continuous mean-centered variables, and their interaction each significantly predicted F/V intake. CONCLUSIONS: Dichotomizing continuous independent variables resulted in distortions of effect sizes across studies, an inability to assess the quadratic effect of health literacy, and an inability to detect the moderating effect of BMI. We discourage researchers from dichotomizing and discretizing continuous independent variables and instead use multiple regression to examine relationships between continuous independent and dependent variables.

Prevalence and correlates of fear of recurrence among oral and oropharyngeal cancer survivors.

PURPOSE: Fear of recurrence (FoR) is a prevalent and difficult experience among cancer patients. Most research has focused on FoR among breast cancer patients, with less attention paid to characterizing levels and correlates of FoR among oral and oropharyngeal cancer survivors. The purpose was to characterize FoR with a measure assessing both global fears and the nature of specific worries as well as evaluate the role of sociodemographic and clinical factors, survivorship care transition practices, lifestyle factors, and depressive symptoms in FoR. METHODS: Three hundred eighty-nine oral and oropharyngeal survivors recruited from two cancer registries completed a survey assessing demographics, cancer treatment, symptoms, alcohol and tobacco use, survivorship care practices, depression, and FoR. RESULTS: Forty percent reported elevated global FoR, with similar percentages for death (46%) and health worries (40.3%). Younger, female survivors and survivors experiencing more physical and depressive symptoms reported more global fears and specific fears about the impact of recurrence on roles, health, and identity, and fears about death. Depression accounted for a large percent of the variance. Lower income was associated with more role and identity/sexuality worries, and financial hardship was associated with more role worries. CONCLUSIONS: FoR is a relatively common experience for oral and oropharyngeal cancer survivors. Many of its correlates are modifiable factors that could be addressed with multifocal, tailored survivorship care interventions. IMPLICATIONS FOR CANCER SURVIVORS: Assessing and addressing depressive symptoms, financial concerns, expected physical symptoms in the first several years of survivorship may impact FoR among oral and oropharyngeal cancer survivors.

Testing the nightmare cognitive arousal processing model.

OBJECTIVE: Posttrauma nightmares are recurring nightmares that begin after a traumatic experience. Research has only recently begun to identify variables that predict posttrauma nightmare occurrences. Research has identified presleep arousal-cognitive (PSA-C) and presleep arousal physiological (PSA-PHYS), sleep onset latency (SOL), and sleep-disordered breathing (SDB) as potential predictors of posttrauma nightmares. However, previous research includes methodological limitations, such as a lack of physiological measures and a homogeneous sample. To replicate previous findings and increase generalizability, the current study investigated predictors of nightmare occurrences in a sample of male inpatient veterans with mixed-trauma history. METHOD: Participants (n = 15) completed an initial assessment battery and seven consecutive days of pre and postsleep diaries, including measures of posttrauma nightmare triggers and posttrauma nightmare occurrences. Portable objective measurements of sleep and presleep states were used to examine sleep quality and physical arousal. RESULTS: Analyses revealed that PSA-C and SOL both predicted posttrauma nightmare occurrences and that PSA-PHYS was significantly higher on nights when nightmares occurred. CONCLUSION: Results replicate earlier research which posits that PSA and SOL play a role in triggering the occurrence of posttrauma nightmares. It should be noted that the sample was relatively small, warranting cautious interpretation of results. However, when taken together with the findings of the replicated study, results could suggest the plausibility of therapies targeting presleep cognitions, SOL, and presleep arousal in the treatment of posttrauma nightmares. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Peers, play, and performance to build social salience in autistic youth: A multisite randomized clinical trial.

OBJECTIVE: Individuals with autism spectrum disorder (ASD) have significant impairment in social competence and reduced social salience. SENSE Theatre, a peer-mediated, theater-based intervention has demonstrated posttreatment gains in face memory and social communication. The multisite randomized clinical trial compared the Experimental (EXP; SENSE Theatre) to an Active Control Condition (ACC; Tackling Teenage Training, TTT) at pretest, posttest, and follow-up. It was hypothesized that the EXP group would demonstrate greater incidental face memory (IFM) and better social behavior (interaction with novel peers) and social functioning (social engagement in daily life) than the ACC group, and posttest IFM would mediate the treatment effect on follow-up social behavior and functioning. METHOD: Two hundred ninety participants were randomized to EXP (N = 144) or ACC (N = 146). Per protocol sample (≥ 7/10 sessions) resulted in 207 autistic children 10-16 years. Event-related potentials measured IFM. Naive examiners measured social behavior (Vocal Expressiveness, Quality of Rapport, Social Anxiety) and functioning (Social Communication). Structural equation modeling was used to assess treatment effects. RESULTS: SENSE Theatre participants showed significantly better IFM (b = .874, p = .039) at posttest, and significant indirect effects on follow-up Vocal Expressiveness a × b = .064, with 90% CI [.014, .118] and Quality of Rapport a × b = .032, with 90% CI [.002, .087] through posttest IFM. CONCLUSIONS: SENSE Theatre increases social salience as reflected by IFM, which in turn affected Vocal Expressiveness and Quality of Rapport. Results indicate that a neural mechanism supporting social cognition and driven by social salience is engaged by the treatment and has a generalized, indirect effect on clinically meaningful functional outcomes related to core symptoms of autism. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Centering categorical predictors in multilevel models: Best practices and interpretation.

The topic of centering in multilevel modeling (MLM) has received substantial attention from methodologists, as different centering choices for lower-level predictors present important ramifications for the estimation and interpretation of model parameters. However, the centering literature has focused almost exclusively on continuous predictors, with little attention paid to whether and how categorical predictors should be centered, despite their ubiquity across applied fields. Alongside this gap in the methodological literature, a review of applied articles showed that researchers center categorical predictors infrequently and inconsistently. Algebraically and statistically, continuous and categorical predictors behave the same, but researchers using them do not, and for many, interpreting the effects of categorical predictors is not intuitive. Thus, the goals of this tutorial article are twofold: to clarify why and how categorical predictors should be centered in MLM, and to explain how multilevel regression coefficients resulting from centered categorical predictors should be interpreted. We first provide algebraic support showing that uncentered coding variables result in a conflated blend of the within- and between-cluster effects of a multicategorical predictor, whereas appropriate centering techniques yield level-specific effects. Next, we provide algebraic derivations to illuminate precisely how the within- and between-cluster effects of a multicategorical predictor should be interpreted under dummy, contrast, and effect coding schemes. Finally, we provide a detailed demonstration of our conclusions with an empirical example. Implications for practice, including relevance of our findings to categorical control variables (i.e., covariates), interaction terms with categorical focal predictors, and multilevel latent variable models, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Required sample size to detect mediation in 3-level implementation studies.

BACKGROUND: Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs (e.g., organization, provider, patient) that are common in implementation research. Using a generalizable Monte Carlo simulation method, this paper examines the sample sizes required to detect mediation in 3-level designs under a range of conditions plausible for implementation studies. METHOD: Statistical power was estimated for 17,496 3-level mediation designs in which the independent variable (X) resided at the highest cluster level (e.g., organization), the mediator (M) resided at the intermediate nested level (e.g., provider), and the outcome (Y) resided at the lowest nested level (e.g., patient). Designs varied by sample size per level, intraclass correlation coefficients of M and Y, effect sizes of the two paths constituting the indirect (mediation) effect (i.e., X→M and M→Y), and size of the direct effect. Power estimates were generated for all designs using two statistical models-conventional linear multilevel modeling of manifest variables (MVM) and multilevel structural equation modeling (MSEM)-for both 1- and 2-sided hypothesis tests. RESULTS: For 2-sided tests, statistical power to detect mediation was sufficient (≥0.8) in only 463 designs (2.6%) estimated using MVM and 228 designs (1.3%) estimated using MSEM; the minimum number of highest-level units needed to achieve adequate power was 40; the minimum total sample size was 900 observations. For 1-sided tests, 808 designs (4.6%) estimated using MVM and 369 designs (2.1%) estimated using MSEM had adequate power; the minimum number of highest-level units was 20; the minimum total sample was 600. At least one large effect size for either the X→M or M→Y path was necessary to achieve adequate power across all conditions. CONCLUSIONS: While our analysis has important limitations, results suggest many of the 3-level mediation designs that can realistically be conducted in implementation research lack statistical power to detect mediation of highest-level independent variables unless effect sizes are large and 40 or more highest-level units are enrolled. We suggest strategies to increase statistical power for multilevel mediation designs and innovations to improve the feasibility of mediation tests in implementation research.

Modelling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model.

A cluster randomized controlled trial (C-RCT) is common in educational intervention studies. Multilevel modelling (MLM) is a dominant analytic method to evaluate treatment effects in a C-RCT. In most MLM applications intended to detect an interaction effect, a single interaction effect (called a conflated effect) is considered instead of level-specific interaction effects in a multilevel design (called unconflated multilevel interaction effects), and the linear interaction effect is modelled. In this paper we present a generalized additive mixed model (GAMM) that allows an unconflated multilevel interaction to be estimated without assuming a prespecified form of the interaction. R code is provided to estimate the model parameters using maximum likelihood estimation and to visualize the nonlinear treatment-by-covariate interaction. The usefulness of the model is illustrated using instructional intervention data from a C-RCT. Results of simulation studies showed that the GAMM outperformed an alternative approach to recover an unconflated logistic multilevel interaction. In addition, the parameter recovery of the GAMM was relatively satisfactory in multilevel designs found in educational intervention studies, except when the number of clusters, cluster sizes, and intraclass correlations were small. When modelling a linear multilevel treatment-by-covariate interaction in the presence of a nonlinear effect, biased estimates (such as overestimated standard errors and overestimated random effect variances) and incorrect predictions of the unconflated multilevel interaction were found.

Self-distancing and Avoidance Mediate the Links Between Trait Mindfulness and Responses to Emotional Challenges.

OBJECTIVES: Mindfulness has been linked to better emotion regulation and more adaptive responses to stress across a number of studies, but the mechanisms underlying these links remain to be fully understood. The present study examines links between trait mindfulness (Five Facets of Mindfulness Questionnaire; FFMQ) and participants' responses to common emotional challenges, focusing specifically on the roles of reduced avoidance and more self-distanced engagement as key potential mechanisms driving the adaptive benefits of trait mindfulness. METHODS: Adults (n = 305, age range: 40-72) from the Second Generation Study of the Harvard Study of Adult Development completed two laboratory-based challenges - public speaking combined with difficult math tasks (the Trier Social Stress Test) and writing about a memory of a difficult moment. State anxiety and sadness were assessed immediately before and after the two stressors. To capture different ways of engaging, measures of self-distancing, avoidance, and persistent worry were collected during the lab session. RESULTS: As predicted, individuals who scored higher on the FFMQ experienced less anxiety and persistent worry in response to the social stressors. The FFMQ was also linked to less anxiety and sadness when writing about a difficult moment. The links between mindfulness and negative emotions after the writing task were independently mediated by self-distanced engagement and lower avoidance. CONCLUSIONS: Affective benefits of trait mindfulness under stress are associated with both the degree and the nature of emotional engagement. Specifically, reduced avoidance and self-distanced engagement may facilitate reflection on negative experiences that is less affectively aversive.

From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.

Coherence Between Feelings and Heart Rate: Links to Early Adversity and Responses to Stress.

Past research suggests that higher coherence between feelings and physiology under stress may confer regulatory advantages. Research and theory also suggest that higher resting vagal tone (rVT) may promote more adaptive responses to stress. The present study examines the roles of response system coherence (RSC; defined as the within-individual covariation between feelings and heart rate over time) and rVT in mediating the links between childhood adversity and later-life responses to acute stressors. Using data from 279 adults from the Second Generation Study of the Harvard Study of Adult Development who completed stressful public speaking and mental arithmetic tasks, we find that individuals who report more childhood adversity have lower RSC, but not lower rVT. We further find that lower RSC mediates the association between adversity and slower cardiovascular recovery. Higher rVT in the present study is linked to less intense cardiovascular reactivity to stress, but not to quicker recovery or to the subjective experience of negative affect after the stressful tasks. Additional analyses indicate links between RSC and mindfulness and replicate previous findings connecting RSC to emotion regulation and well-being outcomes. Taken together, these findings are consistent with the idea that uncoupling between physiological and emotional streams of affective experiences may be one of the mechanisms connecting early adversity to later-life affective responses. These findings also provide evidence that RSC and rVT are associated with distinct aspects of self-regulation under stress. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-020-00027-5.

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects.

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.

A Sandwich Standard Error Estimator for Exploratory Factor Analysis With Nonnormal Data and Imperfect Models.

This article is concerned with standard errors (SEs) and confidence intervals (CIs) for exploratory factor analysis (EFA) in different situations. The authors adapt a sandwich SE estimator for EFA parameters to accommodate nonnormal data and imperfect models, factor extraction with maximum likelihood and ordinary least squares, and factor rotation with CF-varimax, CF-quartimax, geomin, or target rotation. They illustrate the sandwich SEs and CIs using nonnormal continuous data and ordinal data. They also compare SE estimates and CIs of the conventional information method, the sandwich method, and the bootstrap method using simulated data. The sandwich method and the bootstrap method are more satisfactory than the information method for EFA with nonnormal data and model approximation error.

Identifying pathways to quitting smoking via telemedicine-delivered care.

OBJECTIVE: A randomized controlled trial of quitline-like phone counseling (QL) versus telemedicine integrated into primary care (ITM) compared the effectiveness of these modalities for smoking cessation. Study design and components were based on self-determination theory (SDT). The purpose of this study was to test our SDT-based model in which perceived health care provider autonomy support, working alliance, autonomous motivation, and perceived competence were hypothesized to mediate the effects of ITM on smoking cessation. METHOD: Rural smokers (n = 560) were randomized to receive 4 sessions over a 3-month period of either QL or ITM. Follow-up assessments were conducted at Months 3, 6, and 12. The primary outcome was biochemically verified 7-day point prevalence at 12 -months. Structural equation modeling with latent change scores was used for the analysis. RESULTS: Participants in the ITM condition reported greater increases in perceived health care provider autonomy support (PAS) at end of treatment, which in turn was associated with enhanced perceived competence to quit smoking (PC). Increased PC was associated with a higher likelihood of cessation at 12-months. Mediation analysis demonstrated significant indirect effects, including a path from ITM to increases in PAS to increases in PC to cessation at 12-months (indirect effect = 0.0183, 95% confidence interval [.003, .0434]). CONCLUSIONS: When integrated into primary care, ITM may influence smoking cessation by enhancing the extent to which smokers feel supported by their providers and thereby increase their perceived ability to quit. Findings suggest that locating tobacco treatment services in health care provider offices imparts a motivational benefit for cessation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Assessing within- and between-family variations in an expanded measure of childhood adversity.

Previous measures of childhood adversity have enabled the identification of powerful links with later-life wellbeing. The challenge for the next generation of childhood adversity assessment is to better characterize those links through comprehensive, fine-grained measurement strategies. The expanded, retrospective measure of childhood adversity presented here leveraged analytic and theoretical advances to examine multiple domains of childhood adversity at both the microlevel of siblings and the macrolevel of families. Despite the fact that childhood adversity most often occurs in the context of families, there is a dearth of studies that have validated childhood adversity measures on multiple members of the same families. Multilevel psychometric analyses of this childhood adversity measure administered to 1,194 siblings in 500 families indicated that the additional categories of childhood adversity were widely endorsed, and increased understanding of the sources and sequalae of childhood adversity when partitioned into within- and between-family levels. For example, multilevel confirmatory factor analyses (MCFAs) indicated that financial stress, unsafe neighborhood, and parental unemployment were often experienced similarly by siblings in the same families and stemmed primarily from family wide (between-family) sources. On the other hand, being bullied and school stressors were often experienced differently by siblings and derived primarily from individual (within-family) processes. Multilevel structural equation modeling (MSEM) further illuminated differential criterion validity correlations between these categories of childhood adversity with midlife psychological, social, and physical health. Expanded, multidomain, and multilevel measures of childhood adversity appear to hold promise for identifying layered causes and consequences of adverse childhood experiences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Predictors of engagement in post-discharge quitline counseling among hospitalized smokers.

Quitlines provide evidence-based tobacco treatment and multiple calls yield higher quit rates. This study aimed to identify subgroups of smokers with greater quitline engagement following referral during hospitalization. Data were from a randomized clinical trial assessing the effectiveness of fax referral (referral faxed to proactive quitline) versus warm handoff (patient connected to quitline at bedside) (n = 1054). Classification and regression trees analyses evaluated individual and treatment/health system-related variables and their interactions. Among all participants, warm handoff, higher ratings of the tobacco treatment care transition, and being older predicted completing more quitline calls. Among patients enrolled in the quitline, higher transition of care ratings, being older, and use of cessation medication post-discharge predicted completing more calls. Three of the four factors influencing engagement were characteristics of treatment within the hospital (quality of tobacco treatment care transition and referral method) and therapy (use of cessation medications), suggesting potential targets to increase quitline engagement post-discharge.

Maternal Communication in Childhood Cancer: Factor Analysis and Relation to Maternal Distress.

Objective: This study aimed to characterize mothers' communication with their children in a sample of families with a new or newly relapsed pediatric cancer diagnosis, first using factor analysis and second using structural equation modeling to examine relations between self-reported maternal distress (anxiety, depression, and posttraumatic stress) and maternal communication in prospective analyses. A hierarchical model of communication was proposed, based on a theoretical framework of warmth and control. Methods: The sample included 115 children (age 5-17 years) with new or newly relapsed cancer (41% leukemia, 18% lymphoma, 6% brain tumor, and 35% other) and their mothers. Mothers reported distress (Beck Anxiety Inventory, Beck Depression Inventory-II, and Impact of Events Scale-Revised) 2 months after diagnosis (Time 1). Three months later (Time 2), mother-child dyads were video-recorded discussing cancer. Maternal communication was coded with the Iowa Family Interaction Ratings Scales. Results: Confirmatory factor analysis demonstrated poor fit. Exploratory factor analysis suggested a six-factor model (root mean square error of approximation = .04) with one factor reflecting Positive Communication, four factors reflecting Negative Communication (Hostile/Intrusive, Lecturing, Withdrawn, and Inconsistent), and one factor reflecting Expression of Negative Affect. Maternal distress symptoms at Time 1 were all significantly, negatively related to Positive Communication and differentially related to Negative Communication factors at Time 2. Maternal posttraumatic stress and depressive symptoms each predicted Expression of Negative Affect. Conclusions: Findings provide a nuanced understanding of maternal communication in pediatric cancer and identify prospective pathways of risk between maternal distress and communication that can be targeted in intervention.

Anxiety and Depression in Children of Depressed Parents: Dynamics of Change in a Preventive Intervention.

The current study examined effects of a preventive intervention on patterns of change in symptoms of anxiety and depression in a sample of children of depressed parents. Parents with a history of depression (N = 180) and their children (N = 242; 50% female; Mage = 11.38; 74% Euro-American) enrolled in an intervention to prevent psychopathology in youth. Families were randomized to a family group cognitive behavioral intervention (FGCB) or a written information (WI) control condition. Parents and youth completed the Child Behavior Checklist and Youth Self Report at baseline, 6-, 12-, 18-, and 24-month follow up. Youth in the FGCB intervention reported significantly greater declines in symptoms of both anxiety and depression at 6, 12, and 18 months compared to youth in the WI condition. Youth with higher baseline levels of each symptom (e.g., anxiety) reported greater declines in the other symptom (e.g., depression) from 0 to 6 months in the FGCB intervention only. Changes in anxiety symptoms from 0 to 6 months predicted different patterns of subsequent changes in depressive symptoms from 6 to 12 months for the two conditions, such that declines in anxiety preceded and predicted greater declines in depression for FGCB youth but lesser increases in depression for WI youth. Findings inform transdiagnostic approaches to preventive interventions for at-risk youth, suggesting that both initial symptom levels and initial magnitude of change in symptoms are important to understand subsequent patterns of change in response to intervention.

A novel measure of effect size for mediation analysis.

Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirable properties. We also derive an expression for the bias of the sample estimator for the proposed effect size measure and propose an adjusted version of the estimator. We present a Monte Carlo simulation study conducted to examine the finite sampling properties of the adjusted and unadjusted estimators, which shows that the adjusted estimator is effective at recovering the true value it estimates. Finally, we demonstrate the use of the effect size measure with an empirical example. We provide freely available software so that researchers can immediately implement the methods we discuss. Our developments here extend the existing literature on effect sizes and mediation by developing a potentially useful method of communicating the magnitude of mediation. (PsycINFO Database Record