Correcting for collider effects and sample selection bias in psychological research

Colliders, variables that serve as a common outcome of an independent and dependent variable, pose a major challenge in psychological research. Collider variables can induce bias in the estimation of a population relationship of interest when (a) the composition of a research sample is restricted by scores on a collider variable or (b) researchers adjust for a collider variable in their statistical analyses, as they might do for confounder variables. Both cases interfere with the accuracy and generalizability of statistical results. Despite their importance, however, collider effects remain relatively unknown in psychology. This tutorial article summarizes both the conceptual and the mathematical foundation for collider effects and their relevance to psychological research, and then proposes a method to correct for collider bias in cases of restrictive sample selection based on Thorndike's Case III adjustment (1982). Two simulation studies demonstrated Thorndike's correction as a viable solution for correcting collider bias in research studies, even when restriction on the collider variable was extreme and the selected sample size was as low as N = 100. Bias and relative bias results are reported to evaluate how well the correction equation approximates targeted population correlations under a variety of parameter conditions. We illustrate the application of the correction method to a hypothetical study of intelligence and conscientiousness, discuss the applicability of the method to more complex statistical models as a means of detection for collider bias, and provide code for researchers to apply to their own research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A sensitivity analysis for temporal bias in cross-sectional mediation

For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

We describe the following shared data from N=103 healthy adults who completed a broad set cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. Subjects also completed resting state scans. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Is Religion Special?

Religion makes unique claims (e.g., the existence of supernatural agents) not found in other belief systems, but is religion itself psychologically special? Furthermore, religion is related to many domains of psychological interest, such as morality, health and well-being, self-control, meaning, and death anxiety. Does religion act on these domains via special mechanisms that are unlike secular mechanisms? These could include mechanisms such as beliefs in supernatural agents, providing ultimate meaning, and providing literal immortality. We apply a critical eye to these questions of specialness and conclude that although it is clear that religion is psychologically important, there is not yet strong evidence that it is psychologically special, with the possible exception of its effects on health. We highlight what would be required of future research aimed at convincingly demonstrating that religion is indeed psychologically special, including careful definitions of religion and careful attention to experimental design and causal inference.

Intensive longitudinal mediation in Mplus

Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEMs) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (a) stationary models where the indirect effect is constant over time and people, (b) person-specific models where the indirect effect varies across people, (c) dynamic models where the indirect effect varies across time, and (d) cross-classified models where the indirect effect varies across both time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates-which are unique to BLCS models-were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Momentary Self-regulation: Scale Development and Preliminary Validation

BACKGROUND

Self-regulation refers to a person's ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual level; however, individual-level assessments do not allow the examination of dynamic patterns of intraindividual variability in self-regulation and thus cannot aid in understanding potential malleable processes of self-regulation that may occur in response to the daily environment.

OBJECTIVE

This study aims to develop a brief, psychometrically sound momentary self-regulation scale that can be practically administered through participants' mobile devices at a momentary level.

METHODS

This study was conducted in 2 phases. In the first phase, in a sample of 522 adults collected as part of a larger self-regulation project, we examined 23 previously validated assessments of self-regulation containing 594 items in total to evaluate the underlying structure of self-regulation via exploratory and confirmatory factor analyses. We then selected 20 trait-level items to be carried forward to the second phase. In the second phase, we converted each item into a momentary question and piloted the momentary items in a sample of 53 adults over 14 days. Using the results from the momentary pilot study, we explored the psychometric properties of the items and assessed their underlying structure. We then proposed a set of subscale and total score calculations.

RESULTS

In the first phase, the selected individual-level items appeared to measure 4 factors of self-regulation. The factors identified were perseverance, sensation seeking, emotion regulation, and mindfulness. In the second phase of the ecological momentary assessment pilot, the selected items demonstrated strong construct validity as well as predictive validity for health risk behaviors.

CONCLUSIONS

Our findings provide preliminary evidence for a 12-item momentary self-regulation scale comprising 4 subscales designed to capture self-regulatory dynamics at the momentary level.

Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder

Introduction

Self-regulation has been implicated in health risk behaviors and is a target of many health behavior interventions. Despite most prior research focusing on self-regulation as an individual-level trait, we hypothesize that self-regulation is a time-varying mechanism of health and risk behavior that may be influenced by momentary contexts to a substantial degree. Because most health behaviors (e.g., eating, drinking, smoking) occur in the context of everyday activities, digital technologies may help us better understand and influence these behaviors in real time. Using a momentary self-regulation measure, the current study (which was part of a larger multi-year research project on the science of behavior change) used ecological momentary assessment (EMA) to assess if self-regulation can be engaged and manipulated on a momentary basis in naturalistic, non-laboratory settings.

Methods

This one-arm, open-label exploratory study prospectively collected momentary data for 14 days from 104 participants who smoked regularly and 81 participants who were overweight and had binge-eating disorder. Four times per day, participants were queried about momentary self-regulation, emotional state, and social and environmental context; recent smoking and exposure to smoking cues (smoking sample only); and recent eating, binge eating, and exposure to binge-eating cues (binge-eating sample only). This study used a novel, momentary self-regulation measure comprised of four subscales: momentary perseverance, momentary sensation seeking, momentary self-judgment, and momentary mindfulness. Participants were also instructed to engage with Laddr, a mobile application that provides evidence-based health behavior change tools via an integrated platform. The association between momentary context and momentary self-regulation was explored via mixed-effects models. Exploratory assessments of whether recent Laddr use (defined as use within 12 h of momentary responses) modified the association between momentary context and momentary self-regulation were performed via mixed-effects models.

Results

Participants (mean age 35.2; 78% female) in the smoking and binge-eating samples contributed a total of 3,233 and 3,481 momentary questionnaires, respectively. Momentary self-regulation subscales were associated with several momentary contexts, in the combined as well as smoking and binge-eating samples. For example, in the combined sample momentary perseverance was associated with location, positively associated with positive affect, and negatively associated with negative affect, stress, and tiredness. In the smoking sample, momentary perseverance was positively associated with momentary difficulty in accessing cigarettes, caffeine intake, and momentary restraint in smoking, and negatively associated with temptation and urge to smoke. In the binge-eating sample, momentary perseverance was positively associated with difficulty in accessing food and restraint in eating, and negatively associated with urge to binge eat. While recent Laddr use was not associated directly with momentary self-regulation subscales, it did modify several of the contextual associations, including challenging contexts.

Conclusions

Overall, this study provides preliminary evidence that momentary self-regulation may vary in response to differing momentary contexts in samples from two exemplar populations with risk behaviors. In addition, the Laddr application may modify some of these relationships. These findings demonstrate the possibility of measuring momentary self-regulation in a trans-diagnostic way and assessing the effects of momentary, mobile interventions in context. Health behavior change interventions may consider measuring and targeting momentary self-regulation in addition to trait-level self-regulation to better understand and improve health risk behaviors. This work will be used to inform a later stage of research focused on assessing the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and health outcomes.

Clinical Trial Registration

ClinicalTrials.gov, Identifier: NCT03352713.

Teacher's Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation

Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder.

Within- and between-person effects of naltrexone on the subjective response to alcohol and craving: A daily diary investigation

OBJECTIVE

Naltrexone is an effective treatment for heavy drinking among young adults. Laboratory-based studies have shown that naltrexone dampens the subjective response to alcohol and craving. However, few studies have tested naltrexone's dynamic, within-person effects on subjective response and craving among young adults in natural drinking environments.

METHODS

Using daily diary data from a randomized, placebo-controlled study of naltrexone's efficacy in young adults, we examined the between-person effects of treatment condition (i.e., naltrexone vs. placebo) and medication dosage (i.e., daily, targeted, and daily + targeted) on the subjective response to alcohol and craving on drinking days. Multilevel mediation models predicted subjective response and craving from treatment condition (between-person) and medication dosage (within-person), accounting for drinking levels. All effects were disaggregated within and between persons.

RESULTS

At the between-person level, naltrexone directly blunted intense subjective effects (i.e., "impaired", "drunk") and indirectly blunted subjective effects through reduced drinking. Naltrexone was not associated with craving. Between-person effects were not significant after alpha correction, but their effect sizes (bs = 0.14 to 0.17) exceeded the smallest effect size of interest. At the within-person level, taking two (vs. 1) pills was associated with heavier drinking, and taking one (vs. 0) pill was associated with lighter drinking, and lighter drinking was associated with a lower subjective response and craving. Treatment condition did not moderate the within-person effects of dosing on outcomes.

CONCLUSIONS

Our findings suggest that the direct between-person effect of naltrexone was largest on intense subjective responses, blunting feelings of being "drunk" and "impaired". Future research using momentary (rather than daily) assessments could confirm and extend these findings.

A Randomization Permutation Test for Single Subject Mediation

In response to the importance of individual-level effects, the purpose of this paper is to describe the new randomization permutation (RP) test for a mediation mechanism for a single subject. We extend seminal work on permutation tests for individual-level data by proposing a test for mediation for one person. The method requires random assignment to the levels of the treatment variable at each measurement occasion, and repeated measures of the mediator and outcome from one subject. If several assumptions are met, the process by which a treatment changes an outcome can be statistically evaluated for a single subject, using the permutation mediation test method and the permutation confidence interval method for residuals. A simulation study evaluated the statistical properties of the new method suggesting that at least eight repeated measures are needed to control Type I error rates and larger sample sizes are needed for power approaching .8 even for large effects. The RP mediation test is a promising method for elucidating intraindividual processes of change that may inform personalized medicine and tailoring of process-based treatments for one subject.

Mediation analysis methods used in observational research: a scoping review and recommendations

Background

Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.

Methods

We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.

Results

We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.

Conclusions

To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01426-3.

A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies: The AGReMA Statement

Importance

Mediation analyses of randomized trials and observational studies can generate evidence about the mechanisms by which interventions and exposures may influence health outcomes. Publications of mediation analyses are increasing, but the quality of their reporting is suboptimal.

Objective

To develop international, consensus-based guidance for the reporting of mediation analyses of randomized trials and observational studies (A Guideline for Reporting Mediation Analyses; AGReMA).

Design, Setting, and Participants

The AGReMA statement was developed using the Enhancing Quality and Transparency of Health Research (EQUATOR) methodological framework for developing reporting guidelines. The guideline development process included (1) an overview of systematic reviews to assess the need for a reporting guideline; (2) review of systematic reviews of relevant evidence on reporting mediation analyses; (3) conducting a Delphi survey with panel members that included methodologists, statisticians, clinical trialists, epidemiologists, psychologists, applied clinical researchers, clinicians, implementation scientists, evidence synthesis experts, representatives from the EQUATOR Network, and journal editors (n = 19; June-November 2019); (4) having a consensus meeting (n = 15; April 28-29, 2020); and (5) conducting a 4-week external review and pilot test that included methodologists and potential users of AGReMA (n = 21; November 2020).

Results

A previously reported overview of 54 systematic reviews of mediation studies demonstrated the need for a reporting guideline. Thirty-three potential reporting items were identified from 3 systematic reviews of mediation studies. Over 3 rounds, the Delphi panelists ranked the importance of these items, provided 60 qualitative comments for item refinement and prioritization, and suggested new items for consideration. All items were reviewed during a 2-day consensus meeting and participants agreed on a 25-item AGReMA statement for studies in which mediation analyses are the primary focus and a 9-item short-form AGReMA statement for studies in which mediation analyses are a secondary focus. These checklists were externally reviewed and pilot tested by 21 expert methodologists and potential users, which led to minor adjustments and consolidation of the checklists.

Conclusions and Relevance

The AGReMA statement provides recommendations for reporting primary and secondary mediation analyses of randomized trials and observational studies. Improved reporting of studies that use mediation analyses could facilitate peer review and help produce publications that are complete, accurate, transparent, and reproducible.

Statistical Evaluation of Person-Oriented Mediation Using Configural Frequency Analysis

Mediation analysis helps explain how and why two variables are related, providing information for investigating causal processes useful for theoretical and applied research (MacKinnon 2008). Inference from mediation analysis typically applies to the population, but researchers and clinicians are often interested in making inference to individual clients or small sub-populations of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables. A recently proposed method allows for the analysis of person differences as part of mediation. The method from configural frequency analysis, which we call configural frequency mediation, is based on log-linear modeling of contingency tables. The complexity of configural frequency mediation and its use of a causal steps mediation method, may contribute to the lack of application and study of this promising method since its introduction in the literature a decade ago (von Eye et al. 2009, 2010) In this paper we clarify the steps used for configural frequency mediation and report the results of a large statistical simulation study evaluating the method and comparing it to the variable-oriented traditional method using logistic regression analysis. Overall, configural frequency mediation analysis tended to have excessive type I error rates but we describe an alternative approach to configural mediation analysis based on a joint significance test that had adequate performance. We also clarify the decision rules that define configural mediation analysis and develop a test for configural frequency mediation using a joint significance mediation method.

Perturbation practice in multiple sclerosis: Assessing generalization from support surface translations to tether-release tasks

OBJECTIVE

To determine whether improvements in protective stepping experienced after repeated support surface translations generalize to a different balance challenge in people with multiple sclerosis (PwMS) BACKGROUND: MS affects almost 1 million people in the United States and impairs balance and mobility. Perturbation practice can improve aspects of protective stepping in PwMS, but whether these improvements generalize is unknown.

METHODS

Fourteen PwMS completed two visits, 24hrs apart. The balance tasks included tether-release trials and support surface translations on a treadmill eliciting backward protective stepping. Margin of stability, step length, and step latency were calculated. Generalization was assessed via multilevel mediation models (MLMM) with bootstrapping to produce percentile and bias corrected confidence intervals RESULTS: There were no mediated effects for margin of stability or step latency; however, mediation was observed for step length, indicating that participants increased step length throughout the treadmill trials, and this generalized to tether-release trials DISCUSSION: MLMM may be useful for evaluating generalization of motor training to novel balance situations, particularly in small sample sizes. Using these analyses, we observed PwMS generalized improvements in step length, suggesting that aspects of protective step training may translate to improvements in other reactive balance tasks in PwMS.

Using Novel Approaches to Evaluate Behavioral Interventions: Overlooked Significant HIV Prevention Effects in the HPTN 015 Project EXPLORE

BACKGROUND

Mediated and moderated processes that lead to intervention efficacy may underlie results of trials ruled as nonefficacious. The overall purpose of this study was to examine such processes to explain the findings of one of the largest, rigorously conducted behavioral intervention randomized controlled trials, EXPLORE.

METHODS

Four thousand two hundred ninety-five HIV-negative men who have sex with men (MSM) in the United States were randomized in a 2-armed trial. Participants completed follow-up and an HIV test every 6 months up to 48 months. We used multiple and causal mediation analyses to test 5 mediators, including safer sex self-efficacy and condomless receptive anal sex with HIV-positive or status-unknown partners on our primary outcome (HIV seroconversion). We also examined whether intervention effects on the mediators would be moderated by robust correlates of HIV among MSM, including stimulant use.

RESULTS

There were significant effects of the intervention on all hypothesized mediators. Stimulant use moderated the effect on condomless receptive anal sex In stratified multiple mediation models, we found that among MSM with low stimulant use, the intervention significantly prevented HIV by reducing condomless receptive anal sex with HIV-positive or status-unknown partners. Among MSM with higher stimulant use, there were no indirect effects of the intervention on HIV through any of the hypothesized mediators.

CONCLUSION

The results suggest that the null effect found in the original EXPLORE trial might have occurred as a function of previously unexplored mediated and moderated processes. This study illustrates the value of testing mediated and moderated pathways in randomized trials, even in trials ruled out as nonefficacious.

Assessing Potential Outcomes Mediation in HIV Interventions

Knowledge of causal processes through mediation analysis can help improve the effectiveness and reduce costs of public health programs, like HIV prevention and treatment interventions. Advancements in mediation using the potential outcomes framework provide a method for estimating the causal effect of interventions on outcomes via a mediating variable. The purpose of this paper is to provide practical information about mediation and the potential outcomes framework that can enhance data analysis and causal inference for intervention studies. Causal mediation effects are defined and then estimated using data from an HIV intervention randomized trial among people who inject drugs (PWID) in Ukraine. Results from a potential outcomes mediation analysis show that the intervention had a total causal effect on incident HIV infection such that participants in the experimental group were 36% less likely to become infected during the 12-month study than those in the control arm, but that neither self-efficacy nor network communication mediated this effect. Because neither putative mediator was significant, measurement and confounding issues should be investigated to rule out these mediators. Other putative mediators, such as injection frequency, route of administration, or HIV knowledge can be considered. Future research is underway to examine additional, multiple mediators explaining efficacy of the current intervention and sensitivity to confounding effects.

Sequential Bayesian Data Synthesis for Mediation and Regression Analysis

Science is an inherently cumulative process, and knowledge on a specific topic is organized through synthesis of findings from related studies. Meta-analysis has been the most common statistical method for synthesizing findings from multiple studies in prevention science and other fields. In recent years, Bayesian statistics have been put forth as another way to synthesize findings and have been praised for providing a natural framework for update existing knowledge with new data. This article presents a Bayesian method for cumulative science and describes a SAS macro %SBDS for synthesizing findings from multiple studies or multiple data sets from a single study using three different methods: meta-analysis using raw data, sequential Bayesian data synthesis, and a single-level analysis on pooled data. Sequential Bayesian data synthesis and Bayesian statistics in general are discussed in an accessible manner, and guidelines are provided on how researchers can use the accompanying SAS macro for synthesizing data from their own studies. Four alcohol use studies were used to demonstrate how to apply the three data synthesis methods using the SAS macro.

Latent Class Mediation: A Comparison of Six Approaches

Latent class mediation modeling is designed to estimate the mediation effect when both the mediator and the outcome are latent class variables. We suggest using an adjusted one-step approach in which the latent class models for the mediator and the outcome are estimated first to decide on the number of classes, then the latent class models and the mediation model are jointly estimated. We present both an empirical demonstration and a simulation study to compare the performance of this one-step approach to a standard three-step approach with modal assignment (modal) and four different modern three-step approaches. Results from the study indicate that unadjusted modal, which ignores the classification errors of the latent class models, produced biased mediation effects. On the other hand, the adjusted one-step approach and the modern three-step approaches performed well with respect to bias for estimating mediation effects, regardless of measurement quality (i.e., model entropy) and latent class size. Among the three-step approaches we investigated, the maximum likelihood method with modal assignment and the BCH correction with robust standard error estimators are good alternatives to the adjusted one-step approach, given their unbiased standard error estimations.

A Unification of Mediator, Confounder, and Collider Effects

Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper describes the importance of the collider effect as well as the similarities and differences between these three third-variable effects. The single mediator model in which the third variable (T) is a mediator of the independent variable (X) to dependent variable (Y) effect is used to demonstrate how to estimate each third-variable effect. We provide difference in coefficients and product of coefficients estimators of the effects and demonstrate how to calculate these values with real data. Suppression effects are defined for each type of third-variable effect. Future directions and implications of these results are discussed.

Multiple mediation analysis of the peer-delivered Thinking Healthy Programme for perinatal depression: findings from two parallel, randomised controlled trials

BACKGROUND

Low-intensity psychosocial interventions have been effective in targeting perinatal depression, but relevant mechanisms of change remain unknown.

AIMS

To examine three theoretically informed mediators of the Thinking Healthy Programme Peer-delivered (THPP), an evidence-based psychosocial intervention for perinatal depression, on symptom severity in two parallel, randomised controlled trials in Goa, India and Rawalpindi, Pakistan.

METHOD

Participants included pregnant women aged ≥18 years with moderate to severe depression, as defined by a Patient Health Questionnaire 9 (PHQ-9) score ≥10, and were randomised to either THPP or enhanced usual care. We examine whether three prespecified variables (patient activation, social support and mother-child attachment) at 3 months post-childbirth mediated the effects of THPP interventions of perinatal depressive symptom severity (PHQ-9) at the primary end-point of 6 months post-childbirth. We first examined individual mediation within each trial (n = 280 in India and n = 570 in Pakistan), followed by a pooled analysis across both trials (N = 850).

RESULTS

In both site-specific and pooled analyses, patient activation and support at 3 months independently mediated the intervention effects on depressive symptom severity at 6 months, accounting for 23.6 and 18.2% of the total effect of THPP, respectively. The intervention had no effect on mother-child attachment scores, thus there was no evidence that this factor mediated the intervention effect.

CONCLUSIONS

The effects of the psychosocial intervention on depression outcomes in mothers were mediated by the same two factors in both contexts, suggesting that such interventions seeking to alleviate perinatal depression should target both social support and patient activation levels.

DECLARATION OF INTEREST

None.

The measurement of the mediator and its influence on statistical mediation conclusions

In psychology, the causal process between 2 variables can be studied with statistical mediation analysis. To make a causal interpretation about the relation between variables, researchers who use the statistical mediation model make many assumptions about the variables in the model, among which are measurement assumptions about the mediator. For example, researchers often assume that the measure of the mediator yields scores that are reliable and that have a valid interpretation. In this article, we address how several measurement challenges affect the conclusions of statistical mediation analysis, and how researchers can use different psychometric models to study theoretically different causal processes. We use simulated data sets to illustrate how 10 well-fitting and theoretically sound statistical mediation models could significantly detect the indirect effect or miss it entirely depending on how the mediator is represented in the model. In the example, power to detect the indirect effect varied by the amount of true mediator variance that the psychometric model of the mediator was able to isolate. Different strategies to incorporate psychometric methods into mediation research are discussed and future directions are considered. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Extrinsic Convergent Validity Evidence to Prevent Jingle and Jangle Fallacies

In psychology, there have been vast creative efforts in proposing new constructs and developing measures to assess them. Less effort has been spent in investigating construct overlap to prevent bifurcated literatures, wasted research efforts, and jingle-jangle fallacies. For example, researchers could gather validity evidence to evaluate if two measures with the same label actually assess different constructs (jingle fallacy), or if two measures with different labels actually assess the same construct (jangle fallacy). In this paper, we discuss the concept of extrinsic convergent validity, a source of validity evidence demonstrated when two measures of the same construct, or two measures of seemingly different constructs, have comparable correlations with external criteria. We introduce a formal approach to obtain extrinsic convergent validity evidence using tests of dependent correlations and evaluate the tests using Monte Carlo simulations. Also, we illustrate the methods by examining the overlap between the self-control and grit constructs, and the overlap among seven seemingly different measures of the connectedness to nature construct. Finally, we discuss how extrinsic convergent validity evidence supplements other sources of evidence that support validity arguments of construct overlap.

Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables

In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informative priors on statistical properties of point and interval summaries of the mediated effect. This article describes the first examination of using fully conjugate and informative (accurate and inaccurate) priors in Bayesian mediation analysis with latent variables. Results suggest that fully conjugate priors and informative priors with the same relative prior sample sizes have notably different effects at N = 200 and 400, than at N = 50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated effect is nonzero. Recommendations are provided about when to gainfully employ Bayesian mediation analysis with latent variables.

The Correspondence Between Causal and Traditional Mediation Analysis: the Link Is the Mediator by Treatment Interaction

Mediation analysis is a methodology used to understand how and why behavioral phenomena occur. New mediation methods based on the potential outcomes framework are a seminal advancement for mediation analysis because they focus on the causal basis of mediation. Despite the importance of the potential outcomes framework in other fields, the methods are not well known in prevention and other disciplines. The interaction of a treatment (X) and a mediator (M) on an outcome variable (Y) is central to the potential outcomes framework for causal mediation analysis and provides a way to link traditional and modern causal mediation methods. As described in the paper, for a continuous mediator and outcome, if the XM interaction is zero, then potential outcomes estimators of the mediated effect are equal to the traditional model estimators. If the XM interaction is nonzero, the potential outcomes estimators correspond to simple direct and simple mediated contrasts for the treatment and the control groups in traditional mediation analysis. Links between traditional and causal mediation estimators clarify the meaning of potential outcomes framework mediation quantities. A simulation study demonstrates that testing for a XM interaction that is zero in the population can reduce power to detect mediated effects, and ignoring a nonzero XM interaction in the population can also reduce power to detect mediated effects in some situations. We recommend that prevention scientists incorporate evaluation of the XM interaction in their research.

The use of traditional and causal estimators for mediation models with a binary outcome and exposure-mediator interaction

An important recent development in mediation analysis is the use of causal mediation analysis. Causal mediation analysis decomposes the total exposure effect into causal direct and indirect effects in the presence of exposure-mediator interaction. However, in practice, traditional mediation analysis is still most widely used. The aim of this paper is to demonstrate the similarities and differences between the causal and traditional estimators for mediation models with a continuous mediator, a binary outcome, and exposure-mediator interaction. A real-life data example, analytical comparisons, and a simulation study were used to demonstrate the similarities and differences between the traditional and causal estimators. The causal and traditional estimators provide similar indirect effect estimates, but different direct and total effect estimates. Traditional mediation analysis may only be used when conditional direct effect estimates are of interest. Causal mediation analysis is the generally preferred method as its casual effect estimates help unravel causal mechanisms.

Causal Mediation Analysis in the Presence of Post-treatment Confounding Variables: A Monte Carlo Simulation Study

In many disciplines, mediating processes are usually investigated with randomized experiments and linear regression to determine if the treatment affects the outcome through a mediator. However, randomizing the treatment will not yield accurate causal direct and indirect estimates unless certain assumptions are satisfied since the mediator status is not randomized. This study describes methods to estimate causal direct and indirect effects and reports the results of a large Monte Carlo simulation study on the performance of the ordinary regression and modern causal mediation analysis methods, including a previously untested doubly robust sequential g-estimation method, when there are confounders of the mediator-to-outcome relation. Results show that failing to measure and incorporate potential post-treatment confounders in a mediation model leads to biased estimates, regardless of the analysis method used. Results emphasize the importance of measuring potential confounding variables and conducting sensitivity analysis.

Exposure to ethnic discrimination in social media and symptoms of anxiety and depression among Hispanic emerging adults: Examining the moderating role of gender

METHOD

Two hundred Hispanic emerging adults from Arizona (n = 99) and Florida (n = 101) completed a cross-sectional survey, and data were analyzed using hierarchical multiple regression and moderation analyses.

RESULTS

Higher social media discrimination was associated with higher symptoms of depression and generalized anxiety. Moderation analyses indicated that higher social media discrimination was only associated with symptoms of depression and generalized anxiety among men, but not women.

CONCLUSION

This is likely the first study on social media discrimination and mental health among emerging adults; thus, expanding this emerging field of research to a distinct developmental period.

Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata

Mediation analysis is a methodology used to understand how and why an independent variable (X) transmits its effect to an outcome (Y) through a mediator (M). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of mediation analysis. There are several programs available to estimate causal mediation effects, but these programs differ substantially in data set up, estimation, output, and software platform. To compare these programs, an empirical example is presented, and a single mediator model with XM interaction was estimated with a continuous mediator and a continuous outcome in each program. Even though the software packages employ different estimation methods, they do provide similar causal effect estimates for mediation models with a continuous mediator and outcome. A detailed explanation of program similarities, unique features, and recommendations are discussed.

Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation

Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation and the measures used to assess these constructs. To facilitate the integration of cross-disciplinary measures of self-regulation, we estimated product-moment and distance correlations among 60 cross-disciplinary measures of self-regulation (23 self-report surveys, 37 cognitive tasks) and measures of health and substance use based on 522 participants. The correlations showed substantial variability, though the surveys demonstrated greater convergent validity than did the cognitive tasks. Variables derived from the surveys only weakly correlated with variables derived from the cognitive tasks (M = .049, range = .000 to .271 for the absolute value of the product-moment correlation; M = .085, range = .028 to .241 for the distance correlation), thus challenging the notion that these surveys and cognitive tasks measure the same construct. We conclude by outlining several potential uses for this publicly available database of correlations.

A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model

Two methods from the potential outcomes framework - inverse propensity weighting (IPW) and sequential G-estimation - were evaluated and compared to linear regression for estimating the mediated effect in a two-wave design with a randomized intervention and continuous mediator and outcome. Baseline measures of the mediator and outcome can be considered confounders of the follow-up mediator - outcome relation for which adjustment is necessary to eliminate bias. To adjust for baseline measures of the mediator and outcome, IPW uses stabilized inverse propensity weights whereas sequential G-estimation uses regression adjustment. Theoretical differences between the models are described, and Monte Carlo simulations compared the performance of linear regression; IPW without weight truncation; IPW with weights truncated at the 1st/99th, 5th/95th, and 10th/90th percentiles; and sequential G-estimation. Sequential G-estimation performed similarly to linear regression, but IPW provided a biased estimate of the mediated effect, lower power, lower confidence interval coverage, and higher mean squared error. Simulation results show that IPW failed to fully adjust the follow-up mediator - outcome relation for confounding due to the baseline measures. We then compared the mediated effect estimates using data from a randomized experiment evaluating a steroid prevention program for high school athletes. Implications and future directions are discussed.

A Psychometric Analysis of the Brief Self-Control Scale

The Brief Self-Control Scale (BSCS) is a widely used measure of self-control, a construct associated with beneficial psychological outcomes. Several studies have investigated the psychometric properties of the BSCS but have failed to reach consensus. This has resulted in an unstable and ambiguous understanding of the scale and its psychometric properties. The current study sought resolution by implementing scale evaluation approaches guided by modern psychometric literature. Additionally, our goal was to provide a more comprehensive item analysis via the item response theory (IRT) framework. Results from the current study support both unidimensional and multidimensional factor structures for the 13-item version of the BSCS. The addition of an IRT analysis provided a new perspective on item- and test-level functioning. The goal of a more defensible psychometric grounding for the BSCS is to promote greater consistency, stability, and trust in future results.

A Psychometric Evaluation of the Short Grit Scale: A Closer Look at its Factor Structure and Scale Functioning

Grit, the passion and perseverance for long-term goals, has received attention from personality psychologists because it predicts success and academic achievement. Grit has also been criticized as simply another measure of self-control or conscientiousness. A precise psychometric representation of grit is needed to understand how the construct is unique and how it overlaps with other constructs. Previous research suggests that the Short Grit Scale (Grit-S) has several psychometric limitations, such as uncertain factor structure within and across populations, uncertainty about reporting total or subscale scores, and different assessment precision at low and high levels on the construct. We conducted modern psychometric techniques including parallel analysis, measurement invariance, extrinsic convergent validity, and Item Response Theory models on two American samples. Our results suggest that the Grit-S is essentially unidimensional and that there is construct overlap with the self-control construct. Subscale factors were the result of an item doublet, where two items had high correlated uniquenesses, showed similar item information, and were more likely to exhibit measurement bias. Findings replicated across samples. Finally, we discuss recommendations for the use of the Grit-S based on the theoretical interpretation of the unidimensional factor and our empirical findings.

Mediation analysis with binary outcomes: Direct and indirect effects of pro-alcohol influences on alcohol use disorders

A risk factor or intervention (an independent variable) may influence a substance abuse outcome (the dependent variable) indirectly, by affecting an intervening variable (a mediator) that in turn affects that outcome. Mediation analysis is a statistical method commonly used to examine the interrelations among independent, mediating, and dependent variables to obtain the direct and indirect effects of an independent variable on a continuous dependent variable. However, mediation analysis may also be used with binary outcomes, such as a diagnosis of an alcohol use disorder (AUD). Study 1 demonstrated methods of mediation analysis with binary outcomes by examining the direct and indirect effects of pro-alcohol social influences on an AUD, as a function of: (a) the distribution of the independent variable (binary vs. continuous), (b) the frequency of the outcome (non-rare vs. rare), and (c) the effect metric (probability vs. odds ratio). Study 2 was a Monte Carlo (simulation) study of bias in the indirect effects based on estimates from the first study. These methods have wide applicability in addictions research because many key outcomes are binary, and mediation analysis is frequently used to study the causal mechanisms by which interventions and risk factors affect substance abuse.

Uncovering the structure of self-regulation through data-driven ontology discovery

Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here, we derive a psychological ontology from a study of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes associated with self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. We then evaluate predictive power of the psychological measurements and find that while surveys modestly and heterogeneously predict real-world outcomes, tasks largely do not. We conclude that self-regulation lacks coherence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psychological science.

Scientific progress relies on integrating and building on existing knowledge. Here, the authors propose improving cumulative science by developing data-driven ontologies, and they apply this approach to understanding the construct of self-regulation.

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 has grown exponentially over the past 20 years. 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.

Large-scale analysis of test-retest reliabilities of self-regulation measures

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test-retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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.

Bias, Type I Error Rates, and Statistical Power of a Latent Mediation Model in the Presence of Violations of Invariance

When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to systematically examine the impact of mediator noninvariance on the Type I error rates, statistical power, and relative bias in parameter estimates of the mediated effect in the single mediator model. The results of a large simulation study indicated that, in general, the mediated effect was robust to violations of invariance in loadings. In contrast, most conditions with violations of intercept invariance exhibited severely positively biased mediated effects, Type I error rates above acceptable levels, and statistical power larger than in the invariant conditions. The implications of these results are discussed and recommendations are offered.

Reasons for Testing Mediation in the Absence of an Intervention Effect: A Research Imperative in Prevention and Intervention Research

OBJECTIVE

Mediation models are used in prevention and intervention research to assess the mechanisms by which interventions influence outcomes. However, researchers may not investigate mediators in the absence of intervention effects on the primary outcome variable. There is emerging evidence that in some situations, tests of mediated effects can be statistically significant when the total intervention effect is not statistically significant. In addition, there are important conceptual and practical reasons for investigating mediation when the intervention effect is nonsignificant.

METHOD

This article discusses the conditions under which mediation may be present when an intervention effect does not have a statistically significant effect and why mediation should always be considered important.

RESULTS

Mediation may be present in the following conditions: when the total and mediated effects are equal in value, when the mediated and direct effects have opposing signs, when mediated effects are equal across single and multiple-mediator models, and when specific mediated effects have opposing signs. Mediation should be conducted in every study because it provides the opportunity to test known and replicable mediators, to use mediators as an intervention manipulation check, and to address action and conceptual theory in intervention models.

CONCLUSIONS

Mediators are central to intervention programs, and mediators should be investigated for the valuable information they provide about the success or failure of interventions.

Applying novel technologies and methods to inform the ontology of self-regulation

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

SAS® Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models

Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the effect of an independent variable to a dependent variable. Because it is used in many fields, there have been rapid developments in statistical mediation. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effects. Causal inference is particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator. The focus of this paper is on updating three existing SAS® macros (%TWOWAVEMED, %TWOWAVEMONTECARLO, and %TWOWAVEPOSTPOWER, presented at SAS® Global Forum 2017) in two important ways. First, the macros are updated to incorporate new cutting-edge methods for estimating longitudinal mediated effects from the Potential Outcomes Framework for causal inference. The two new methods are inverse-propensity weighting, an application of propensity scores, and sequential G-estimation. The causal inference methods are revolutionary because they frame the estimation of mediated effects in terms of differences in potential outcomes, which align more naturally with how researchers think about causal inference. Second, the macros are updated to estimate mediated effects across three waves of data. The combination of these new causal inference methods and three waves of data enable researchers to test how causal mediated effects develop and maintain over time.

Tutorial: The practical application of longitudinal structural equation mediation models in clinical trials

The study of mediation of treatment effects, or how treatments work, is important to understanding and improving psychological and behavioral treatments, but applications often focus on mediators and outcomes measured at a single time point. Such cross-sectional analyses do not respect the implied temporal ordering that mediation suggests. Clinical trials of treatments often provide repeated measures of outcomes and, increasingly, of mediators as well. Repeated measurements allow the application of various types of longitudinal structural equation mediation models. These provide flexibility in modeling, including the ability to incorporate some types of measurement error and unmeasured confounding that can strengthen the robustness of findings. The usual approach is to identify the most theoretically plausible model and apply that model. In the absence of clear theory, we put forward the option of fitting a few theoretically plausible models, providing a type of sensitivity analysis for the mediation hypothesis. In this tutorial, we outline how to fit several longitudinal mediation models, including simplex, latent growth and latent change models. This will allow readers to learn about one type of model that is of interest, or about several alternative models, so that they can take this sensitivity approach. We use the Pacing, Graded Activity, and Cognitive Behavioral Therapy: A Randomized Evaluation (PACE) trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) as a motivating example and describe how to fit and interpret various longitudinal mediation models using simulated data similar to those in the PACE trial. The simulated data set and Mplus code and output are provided. (PsycINFO Database Record

Family Support and Family Negativity as Mediators of the Relation between Acculturation and Postpartum Weight in Low-Income Mexican-Origin Women

BACKGROUND

Obesity presents a significant health concern among low-income, ethnic minority women of childbearing age.

PURPOSE

The study investigated the influence of maternal acculturation, family negativity, and family support on postpartum weight loss among low-income Mexican-origin women.

METHODS

Low-income Mexican-origin women (N=322; 14% born in the U.S.) were recruited from a prenatal clinic in an urban area of the Southwest U.S. Acculturation was assessed during a prenatal home visit (26-38 weeks gestation), and post-birth family support and general family negativity were assessed at 6 weeks postpartum. Objective maternal weight measures were obtained at five time points across the first postpartum year.

RESULTS

Higher acculturation predicted higher family support and family negativity. Higher family support predicted decreasing weight across the first postpartum year, and higher family negativity predicted higher weight at 6 weeks postpartum and increasing weight across the first postpartum year. In combination, family negativity and support mediated the impact of acculturation on postpartum weight gain.

CONCLUSIONS

Cultural and family-related factors play a significant role in postpartum weight gain and loss for low-income Mexican-origin women.

Confounding in statistical mediation analysis: What it is and how to address it

Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses. (PsycINFO Database Record

Empiric validation of a process for behavior change

Most behavior change trials focus on outcomes rather than deconstructing how those outcomes related to programmatic theoretical underpinnings and intervention components. In this report, the process of change is compared for three evidence-based programs' that shared theories, intervention elements and potential mediating variables. Each investigation was a randomized trial that assessed pre- and post- intervention variables using survey constructs with established reliability. Each also used mediation analyses to define relationships. The findings were combined using a pattern matching approach. Surprisingly, knowledge was a significant mediator in each program (a and b path effects [p<0.01]). Norms, perceived control abilities, and self-monitoring were confirmed in at least two studies (p<0.01 for each). Replication of findings across studies with a common design but varied populations provides a robust validation of the theory and processes of an effective intervention. Combined findings also demonstrate a means to substantiate process aspects and theoretical models to advance understanding of behavior change.