What CBT Modules Work Best for Whom? Identifying Subgroups of Depressed Youths by Their Differential Response to Specific Modules.

Prior research suggests that the effects of specific cognitive-behavioral therapy (CBT) modules on symptom outcomes can be estimated. We conducted a study utilizing idiographic and nomothetic methods to clarify which CBT modules are most effective for youth depression, and for whom they are most effective. Thirty-five youths received modular CBT for depression. Interrupted time series models estimated whether the introduction of each module was associated with changes in internalizing symptoms, whereby significant symptom reduction would suggest a therapeutic response to the module. Regression models were used to explore whether participant characteristics predicted subgroups of youths based on their estimated response to certain types (e.g., cognitive) of modules, and whether group membership was associated with posttreatment outcomes. Thirty youths (86%) had at least one module associated with a significant change in internalizing symptoms from premodule delivery to postmodule delivery. The specific modules associated with these changes varied across youths. Behavioral activation was most frequently associated with symptom decreases (34% of youths). No participant characteristics predicted estimated response to module type, and group membership was not significantly associated with posttreatment outcomes. Youths display highly heterogeneous responses to treatment modules, indicating multiple pathways to symptom improvement for depressed youths.

Comparative genomics of Giardia duodenalis sub-assemblage AI beaver (Be-2) and human (WB-C6) strains show remarkable homozygosity, sequence similarity, and conservation of VSP genes.

Giardia duodenalis, a major cause of waterborne infection, infects a wide range of mammalian hosts and is subdivided into eight genetically well-defined assemblages named A through H. However, fragmented genomes and a lack of comparative analysis within and between the assemblages render unclear the molecular mechanisms controlling host specificity and differential disease outcomes. To address this, we generated a near-complete de novo genome of AI assemblage using the Oxford Nanopore platform by sequencing the Be-2 genome. We generated 148,144 long-reads with quality scores of > 7. The final genome assembly consists of only nine contigs with an N50 of 3,045,186 bp. This assembly agrees closely with the assembly of another strain in the AI assemblage (WB-C6). However, a critical difference is that a region previously placed in the five-prime region of Chr5 belongs to Chr4 of Be-2. We find a high degree of conservation in the ploidy, homozygosity, and the presence of cysteine-rich variant-specific surface proteins (VSPs) within the AI assemblage. Our assembly provides a nearly complete genome of a member of the AI assemblage of G. duodenalis, aiding population genomic studies capable of elucidating Giardia transmission, host range, and pathogenicity.

Breaking the silence of sharing data in medical research.

Data sharing is highly advocated in the scientific community, with numerous organizations, funding agencies, and journals promoting transparency and collaboration. However, limited research exists on actual data sharing practices. We conducted a comprehensive analysis of the intent to share individual participant data (IPD) in a total of 313,990 studies encompassing clinical trials and observational studies obtained from ClinicalTrials.gov, spanning the period from 2000 to 2023. Our study found that only 10.3% of principal investigators (PIs) expressed intent to share IPD. Clinical trials were more likely to share data than observational studies (odds ratio, OR = 1.98, 95% CI: 1.92-2.04). Large sample size studies were 1.69 times more likely to share data than small ones (95% CI: 1.65-1.73). Studies registered after 2018 were 1.6 times more likely to share data (95% CI: 1.57-1.64) than before 2019. NIH and other US Federal agency-funded studies had 1.49 times higher odds of sharing data (95% CI: 1.43-1.55) than other funders. USA-based studies were 1.53 times more likely to share data (95% CI: 1.49-1.57) than out of USA. Biological trials were 1.58 times more likely to share data than drug and other trials (95% CI: 1.51-1.66). Phase III trials had the highest odds, 2.47 times, of sharing data (95% CI: 2.38-2.56) than non-Phase III trials.

Exploring a Potential Interaction Between the Effect of Specific Maternal Smoking Patterns and Comorbid Antenatal Depression in Causing Postpartum Depression.

Purpose: To explore a potential interaction between the effect of specific maternal smoking patterns and the presence of antenatal depression, as independent exposures, in causing postpartum depression (PPD). Methods: This case-control study of participants with singleton term births (N = 51220) was based on data from the 2017-2018 Pregnancy Risk Assessment Monitoring System. Multivariable log-binomial regression models examined the main effects of smoking patterns and self-reported symptoms of antenatal depression on the risk of PPD on the adjusted risk ratio (aRR) scale and tested a two-way interaction adjusting for covariates selected in a directed acyclic graph (DAG). The interaction effects were measured on the additive scale using relative excess risk due to interaction (RERI), the attributable proportion of interaction (AP), and the synergy index (SI). Causal effects were defined in a counterfactual framework. The E-value quantified the potential impact of unobserved/unknown covariates, conditional on observed covariates. Results: Among 6841 women in the sample who self-reported PPD, 35.7% also reported symptoms of antenatal depression. Out of 3921 (7.7%) women who reported smoking during pregnancy, 32.6% smoked at high intensity (≥10 cigarettes/day) in all three trimesters and 36.6% had symptoms of antenatal depression. The main effect of PPD was the strongest for women who smoked at high intensity throughout pregnancy (aRR 1.65; 95% CI: 1.63, 1.68). A synergistic interaction was detected, and the effect of all maternal smoking patterns was augmented, particularly in late pregnancy for Increasers and Reducers. Conclusion: Strong associations and interaction effects between maternal smoking patterns and co-occurring antenatal depression support smoking prevention and cessation interventions during pregnancy to lower the likelihood of PPD.

A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.

OBJECTIVES: Clinical trial data sharing is crucial for promoting transparency and collaborative efforts in medical research. Differential privacy (DP) is a formal statistical technique for anonymizing shared data that balances privacy of individual records and accuracy of replicated results through a "privacy budget" parameter, ε. DP is considered the state of the art in privacy-protected data publication and is underutilized in clinical trial data sharing. This study is focused on identifying ε values for the sharing of clinical trial data. MATERIALS AND METHODS: We analyzed 2 clinical trial datasets with privacy budget ε ranging from 0.01 to 10. Smaller values of ε entail adding greater amounts of random noise, with better privacy as a result. Comparison of rates, odds ratios, means, and mean differences between the original clinical trial datasets and the empirical distribution of the DP estimator was performed. RESULTS: The DP rate closely approximated the original rate of 6.5% when ε > 1. The DP odds ratio closely aligned with the original odds ratio of 0.689 when ε ≥ 3. The DP mean closely approximated the original mean of 164.64 when ε ≥ 1. As ε increased to 5, both the minimum and maximum DP means converged toward the original mean. DISCUSSION: There is no consensus on how to choose the privacy budget ε. The definition of DP does not specify the required level of privacy, and there is no established formula for determining ε. CONCLUSION: Our findings suggest that the application of DP holds promise in the context of sharing clinical trial data.

A novel approach to estimate moderated treatment effects and moderated mediated effects with continuous moderators.

Moderation analysis is used to study under what conditions or for which subgroups of individuals a treatment effect is stronger or weaker. When a moderator variable is categorical, such as assigned sex, treatment effects can be estimated for each group resulting in a treatment effect for males and a treatment effect for females. If a moderator variable is a continuous variable, a strategy for investigating moderated treatment effects is to estimate conditional effects (i.e., simple slopes) via the pick-a-point approach. When conditional effects are estimated using the pick-a-point approach, the conditional effects are often given the interpretation of "the treatment effect for the subgroup of individuals…." However, the interpretation of these conditional effects as subgroup effects is potentially misleading because conditional effects are interpreted at a specific value of the moderator variable (e.g., +1 SD above the mean). We describe a simple solution that resolves this problem using a simulation-based approach. We describe how to apply this simulation-based approach to estimate subgroup effects by defining subgroups using a range of scores on the continuous moderator variable. We apply this method to three empirical examples to demonstrate how to estimate subgroup effects for moderated treatment and moderated mediated effects when the moderator variable is a continuous variable. Finally, we provide researchers with both SAS and R code to implement this method for similar situations described in this paper. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Acculturative Stress and Self-rated Health among Hispanic Emerging Adults: Examining the Moderating Effects of the Social Environment and Social Support.

Little is known about the impact of sociocultural stressors such as acculturative stress on self-rated health among Hispanics. We aimed to examine (a) associations between acculturative stress and self-rated health, and (b) the moderating effects of the community of settlement (i.e., Maricopa County, AZ and Miami-Dade County, FL) and social support on the association between acculturative stress and self-rated health. A hierarchical multiple regression model and moderation analyses were conducted using a cross-sectional sample of 200 Hispanic emerging adults from Arizona and Florida. Findings indicate that higher levels of pressure to acculturate are associated with lower levels of self-rated health. Community of settlement functioned as a moderator whereby pressure to acculturate was only associated with lower levels of self-rated health in Maricopa County. Lastly, a three-way interaction indicated that emotional social support mitigated the association between pressure to acculturate and self-rated health in Maricopa County. This study highlights the importance of accounting for community of settlement when examining associations between acculturative stress and health-related outcomes. A finding that may have implications for interventions is that social support may help to counteract the effects of acculturative stress.

Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis.

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure-mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

Causal mediation effects in single case experimental designs.

Single case experimental designs (SCEDs) are used to test treatment effects in a wide range of fields and consist of repeated measurements for a single case throughout one or more baseline phases and throughout one or more treatment phases. Recently, mediation analysis has been applied to SCEDs. Mediation analysis decomposes the total treatment-outcome effect into a direct and indirect effect, and therefore aims to unravel the causal processes underlying treatment-outcome effects. The most recent methodological advancement for mediation analysis is the development of causal mediation analysis methodology which clarifies the necessary causal assumptions for mediation analysis. The goal of this article is to derive the causal mediation effects and corresponding standard errors based on piecewise linear regression models for the mediator and outcome and to evaluate the performance of these regression estimators and standard errors. Whereas previous studies estimated the direct and indirect effects as either the change in level or change in trend, we showed that the causal direct and indirect effects incorporate both the change in level and change in trend. Based on our simulation study we showed that for the causal indirect effects, Monte Carlo confidence intervals provided accurate (i.e., p = .05) Type I error rates and higher statistical power than normal theory confidence intervals. For the causal direct effects and total effect, normal theory confidence intervals provided accurate Type I error rates and higher statistical power than the Monte Carlo confidence intervals. Limitations and future directions are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Accommodating a Latent XM Interaction in Statistical Mediation Analysis.

Statistical mediation analysis is used in the social sciences and public health to uncover potential mechanisms, known as mediators, by which a treatment led to a change in an outcome. Recently, the estimation of the treatment-by-mediator interaction (i.e., the XM interaction) has been shown to play a pivotal role in understanding the equivalence between the traditional mediation effects in linear models and the causal mediation effects in the potential outcomes framework. However, there is limited guidance on how to estimate the XM interaction when the mediator is latent. In this article, we discuss eight methods to accommodate latent XM interactions in statistical mediation analysis, which fall in two categories: using structural models (e.g., latent moderated structural equations, Bayesian mediation, unconstrained product indicator method, multiple-group models) or scoring the mediator prior to estimating the XM interaction (e.g., summed scores and factor scores, with and without attenuation correction). Simulation results suggest that finite-sample bias is low, type 1 error rates and coverage of percentile bootstrap confidence intervals and Bayesian credible intervals are close to the nominal values, and statistical power is similar across approaches. The methods are demonstrated with an applied example, syntax is provided for their implementation, and general considerations are discussed.

Do specific modules of cognitive behavioral therapy for depression have measurable effects on youth internalizing symptoms? An idiographic analysis.

OBJECTIVE: Understanding the efficacy of each module of cognitive behavioral therapy (CBT) may inform efforts to improve outcomes for youth depression, but effects of specific modules have been difficult to examine. Idiographic interrupted time series models offer a robust way to estimate module effects on an individual's symptoms. This study examined the association of specific CBT modules for depression on internalizing symptoms among depressed youths who received modular CBT in a randomized trial. METHODS: Individual models were created for three youths who met study criteria. Youths completed weekly symptom reports, and clinicians completed records of modules delivered. First order auto-regressive models quantified the change in average internalizing symptom severity between pre- and post-module delivery. RESULTS: All youths had 1-3 modules that were significantly associated with symptom reduction and 1-3 modules associated with deterioration. The 5 modules associated with improvement in at least one youth also lacked association (engagement, relaxation, cognitive reframing), or were associated with worsening (activity selection, parent psychoeducation) in others. Seven modules showed no measurable benefit, or detriment to any youth. CONCLUSION: This study demonstrated that specific modules have measurable effects, but more work is needed to build an evidence base of specific module effects to inform treatment personalization for youth depression.

Time lags and time interactions in mixed effects models impacted longitudinal mediation effect estimates.

OBJECTIVES: Longitudinal mediation effects can be estimated with mixed effects models. Mixed effects models are versatile, as they accommodate the estimation of contemporaneous, lagged, time-independent, and time-dependent effects. However, the inclusion of time lags and time interactions in mixed effects models for longitudinal mediation analysis has received little attention. This article demonstrates how time lags and time interactions in mixed effects models affect the interpretation of longitudinal mediation effect estimates. STUDY DESIGN AND SETTING: We used a data example from the Amsterdam Growth and Health Longitudinal Study to illustrate how the inclusion of time lags and time interactions in mixed effects models may affect the size and interpretation of longitudinal mediation effect estimates. RESULTS: The chosen time lags between the determinant, mediator, and outcome influenced the size and interpretation of the mediation effect estimates. Furthermore, time interactions can be used to model linear or nonlinear development of the mediation effects over time. CONCLUSION: The inclusion of time lags and time interactions should be considered when estimating longitudinal mediation effects based on mixed effects models, as this enables the estimation of lagged and time-dependent effects.

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.

Brief Report: Prevalence and Severity of Auditory Sensory Over-Responsivity in Autism as Reported by Parents and Caregivers.

Auditory sensory over-responsivity (aSOR) is a frequently reported sensory feature of autism spectrum disorders (ASD); however, there is little consensus regarding its prevalence and severity. This cross-sectional study uses secondary data from the Autism Diagnostic Interview-Revised (ADI-R; Item 72: undue sensitivity to noise) housed in the US National Institute of Mental Health Data Archives to identify prevalence and severity of aSOR. Of the 4104 subjects with ASD ages 2-54 (M = 9, SD = 5.8) who responded to item 72, 60.1% (n = 1876) had aSOR currently (i.e., point prevalence) and 71.1% (n = 2221) reported having aSOR ever (i.e., lifetime prevalence). aSOR prevalence and severity were affected by age, but there were no associations with sex.

Predictors of Treatment Engagement and Outcome Among Adolescents With Attention-Deficit/Hyperactivity Disorder: An Integrative Data Analysis.

OBJECTIVE: To identify patient- and treatment-level factors that predict intervention engagement and outcome for adolescents with attention-deficit/hyperactivity disorder (ADHD), guiding efforts to enhance care. METHOD: Integrative data analysis was used to pool data from 4 randomized controlled trials of adolescent ADHD treatment with participants (N = 854) receiving various evidence-based behavioral therapy packages in 5 treatment arms (standard [STANDARD], comprehensive [COMP], engagement-focused [ENGAGE]), community-based usual care (UC), or no treatment (NOTX). Participants also displayed varying medication use patterns (negligible, inconsistent, consistent) during the trial. Regression and latent growth curve analyses examined treatment- and patient-level predictors of engagement and outcome. RESULTS: Compared with COMP, ENGAGE was associated with higher parent engagement in behavioral therapy (d = 1.35-1.73) when delivered in university, but not community, clinics. Under some conditions, ENGAGE also predicted youth engagement in behavioral therapy (d = 1.21) and lower likelihood of negligible medication use (odds ratio = 0.49 compared with NOTX). UC was associated with poorer parent engagement compared with COMP (d = -0.59) and negligible medication use (odds ratio = 2.29) compared with NOTX. Compared with COMP, ENGAGE (in university settings) was consistently associated with larger ADHD symptom improvements (d = 0.41-0.83) at 6-month follow-up and sometimes associated with larger grade point average (d = 0.68) and parent-teen conflict (d = 0.41) improvements. Consistent medication use during behavioral therapy was associated with larger improvements in ADHD symptoms (d = 0.28) and parent-teen conflict (d = 0.25-0.36). An ADHD+internalizing clinical profile predicted larger improvements in grade point average (d = 0.45). Family adversity predicted poorer parent and youth engagement (rate ratio = 0.90-0.95), negligible medication use (odds ratio = 1.22), and smaller improvements in grade point average (d = -0.23). African American race predicted smaller improvements in parent-teen conflict (d = -0.49). CONCLUSION: Engagement-focused behavioral therapy and consistent medication use most frequently predicted stronger clinical engagement and outcomes for adolescents with ADHD. Youths who are African American or who experience family adversity may demonstrate treatment-related disparities for certain outcomes; youths with ADHD+internalizing symptoms may demonstrate excellent academic outcomes following behavioral therapy. DATA SHARING: The full ADHD TIDAL dataset is publicly available through the National Data Archive (https://nda.nih.gov), including a data dictionary. The study protocol is also publicly available: https://doi.org/10.1186/s12888-020-02734-6.

The Effect of Noninvariance on the Estimation of the Mediated Effect in the Two-Wave Mediation Model.

The two-wave mediation model is the most suitable model for examining mediation effects in a randomized intervention and includes measures taken at pretest and posttest. When using self-report measures, the meaning of responses may change for the treatment group over the course of the intervention and result in noninvariance across groups at posttest, a phenomenon referred to as response shift. We investigate how the mediated effect would be impacted by noninvariance when using sum scores (i.e., assuming invariance). In a Monte Carlo simulation study, the magnitude and proportion of items that had noninvariant intercepts, the direction of noninvariance, number of items, effect size of the mediated effect and sample size were varied. Results showed increased Type I and Type II errors due to a biased estimate of the intervention effect on the mediator resulting from noninvariance. Thus, measurement noninvariance could lead to erroneous conclusions about the process underlying the intervention.

Risk of large for gestational age births at early, full and late term in relation to pre-pregnancy body mass index: Mediation by gestational diabetes status.

BACKGROUND: Maternal pre-pregnancy body mass index (BMI) is strongly associated with infant birthweight and the risk differs in pregnancies complicated by gestational diabetes (GDM). OBJECTIVES: To examine the risk of large for gestational age (LGA) (≥97th percentile) singleton births at early term, full term and late term in relation to maternal pre-pregnancy BMI status mediated through GDM. METHODS: We analysed data from the 2018 U.S. National Vital Statistics Natality File restricted to singleton term births (N = 3,229,783). In counterfactual models for causal inference, we estimated the total effect (TE), natural direct effect (NDE) and natural indirect effect (NIE) for the association of pre-pregnancy BMI with subcategories of LGA births at early, full and late term mediated through GDM, using log-binomial regression and adjusting for race/ethnicity, age, education, parity and infant sex. Proportion mediated was calculated on the risk difference scale and potential unmeasured confounders were assessed using the E-value. RESULTS: Overall, 6.4% of women had GDM, and there were 3.6% LGA singleton term births. The highest prevalence of GDM was among pre-gestational overweight/obesity that also had the highest rates of LGA births at term. The TE estimates for the risk of LGA births were the strongest across women with higher pre-pregnancy BMI compared to women with normal pre-pregnancy BMI. The NDE estimates were higher than the NIE estimates for overweight/obese BMI status. The proportion mediated, which answers the causal question to what extent the total effect of the association between pre-pregnancy BMI and LGA births is accounted for through GDM, was the highest (up to 16%) for early term births. CONCLUSIONS: Term singleton births make up the largest proportion in a cohort of newborns. While the percentage mediated through GDM was relatively small, health risks arising from pre-pregnancy overweight, and obesity can be substantial to both mothers and their offspring.

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.