Stepped-wedge cluster randomized trials (SW-CRTs) are one-way crossover trials that randomize clusters (i.e., groups) of individuals to the time point (period) at which an intervention is introduced into the cluster. In these designs, the intervention under evaluation is introduced into all of the clusters by the end of the study in a series of "steps." Analysis of SW-CRTs using marginal models provides a population-averaged interpretation of the estimated intervention effect and flexible specification of the within-cluster, marginal pairwise association structure; the latter has practical application in reporting intraclass (i.e., pairwise) correlations and calculating power for CRTs. Despite these features, use of marginal modeling of SW-CRTs has been mostly limited to applications with working independence and simple exchangeable correlation structures that are suboptimal for multi-period CRTs when correlation among responses decays over time. However, there have been many methodological developments in marginal modeling of SW-CRTs over the past fifteen years, particularly on (i) multi-parameter, within-cluster correlation structures; (ii) paired generalized estimating equations (GEE) for simultaneous estimation of mean and correlation parameters with standard errors; and, when the number of clusters is small, (iii) corrections to reduce the bias of variance estimators, and that of correlation estimates using matrix-adjusted estimating equations (MAEE). The goal of the current tutorial is to survey these newer developments and to provide case studies to enable applied researchers to implement GEE/MAEE for marginal model analysis of SW-CRTs, with application to both cohorts and designs with repeated cross-sectional samples. The methods are also applicable to multi-period, parallel-arm and cluster-crossover CRTs.

Accurate power and sample size (PSS) calculations are essential for designing studies that use quasi-likelihood (QL) models, which extend generalized linear models (GLMs) to settings where the full distribution of the outcome is not specified. Traditional PSS approaches often rely on restrictive distributional assumptions, limiting their applicability when responses have non-standard distributions, variance functions are misspecified, or when predictors exhibit complex dependence structures. Building on recent advances in effect size measures for PSS - specifically, 2 Standard Deviations in the Linear Predictor (2SLiP) and Pseudo-Partial $R^2$ (P2R2) - developed with interpretability in mind, this paper extends and evaluates these effect size measures in the QL framework, keying in particular on their utility in PSS. We assess their empirical performance for the Wald test and then extend to the score test through extensive simulations across diverse outcome types, link functions, and variance structures. To illustrate practical utility, we applied these effect size measures to survey data on frontline health care workers from \citet{cahill2022occupational} to quantify the association between perceived personal protective equipment adequacy and mental health outcomes during the COVID-19 pandemic, adjusting for covariates. Our findings demonstrate that both 2SLiP and P2R2 provide robust and interpretable alternatives to traditional methods, maintaining accuracy with minimal distributional assumptions and enhancing the flexibility of PSS for realistic study designs.

Accurate power and sample size calculations are essential in study planning, yet they are often difficult to carry out for quasi-likelihood (QL) models. Traditional power and sample size (PSS) approaches often rely on restrictive distributional assumptions, limiting their applicability when responses have non-standard distributions, variance functions are misspecified, or when covariates exhibit complex dependence structures. We examine whether two effect size measures—2 Standard Deviations in the Linear Predictor (2SLiP) and Pseudo-Partial R^2 (P2R2)—originally developed for Wald tests involving generalized linear models, are effective at power and sample size calculations in the QL framework. Through extensive simulations across diverse outcome types, link functions, and variance structures, we assess their performance under Wald tests and explore whether they remain useful for score tests. To illustrate practical utility, we apply these effect size measures to survey data on frontline health care workers to quantify the association between perceived personal protective equipment (PPE) adequacy and burnout risk during the COVID-19 pandemic, adjusting for covariates. We show that the two generalized linear model (GLM)-based effect sizes are fundamentally moment-based objects, and therefore extend directly to QL models. Across all simulation settings, both measures remained accurate, with sample size and power estimates within 3

Rationale: The variation of chronic obstructive pulmonary disease (COPD) population-level exacerbation rates at fine spatial scales is unclear but is important for understanding the potential role of neighborhood-level factors in risk for COPD exacerbations. We aim to describe the spatial distribution of emergency department (ED) visits, hospitalizations, and readmissions for acute exacerbations of COPD (AECOPDs) across census tracts in Travis County, Texas, and understand the neighborhood characteristics that may contribute to census tract-level morbidity. Methods: We used the Texas Health Care Information Collection data set and the U.S. Census Bureau's 2019 American Community Survey to calculate census tract-specific population-based incidence rates (PBIRs) and readmission rates from January 2016 through December 2020. Conditional autoregressive models were used to map estimated PBIRs of acute care use outcomes across census tracts. We then examined associations of neighborhood characteristics with census tract-level rates of acute care use. Results: Census tract-level incidence rates of COPD-related ED visits and hospital admissions exhibited spatial patterning across Travis County. In contrast, there was less spatial patterning of census tract-level readmission rates across census tracts. Several census tract demographic, socioeconomic, and built environment characteristics were associated with census tract-level COPD-related ED visit and hospitalization rates, but not with COPD readmission rates. Conclusion: There is a spatial pattern of ED visit and hospitalization PBIRs for AECOPDs within Travis County and several associations with neighborhood characteristics. Readmission rates did not exhibit clear spatial patterning across census tracts and did not have similar associations with neighborhood characteristics.

Background Rates of viral-associated asthma exacerbations typically increase when children return to school after the summer holiday. The effect of neighborhood disadvantage on this back-to-school increase in viral-associated asthma exacerbations is unknown. Objective We aimed to evaluate the effect of neighborhood disadvantage on the back-to-school increase in asthma-related emergency department (ED) visits. Methods Administrative health records were used to define population-based incidence rates of asthma-related ED visits by week and census tract among children aged 5 to 17 years. Using Bayesian regression models of incidence rates, we evaluated additive interactions between a 7-week back-to-school period (relative to a 6-week summer period) and neighborhood disadvantage (eg, captured by the Social Vulnerability Index [SVI]). Results In census tracts with low social vulnerability (SVI = 2), rates of asthma-related ED visits increased by an average of 378 (95% credible interval [CrI] = 358-398) per 100,000 person years when children went back to school. Relative to the back-to-school increase in tracts with low social vulnerability, in tracts with moderate social vulnerability (SVI = 5) there was an excess of 199 asthma-related ED visits per 100,000 person years (95% CrI = 183-214). Relative to the back-to-school increase in tracts with moderate social vulnerability, in tracts with high social vulnerability (SVI = 8), there was an excess of 303 (95% CrI = 268-339) asthma-related ED visits per 100,000 person years. Conclusion Greater levels of neighborhood disadvantage were associated with a greater increase in the rate of asthma-related ED visits when children returned to school. Neighborhood disadvantage may modify the back-to-school increase in asthma exacerbations by increasing the risk of upper respiratory viral infections.

Objective Feasibility evaluation of Nutri, a clinical decision support software for brief diet counseling by primary care providers (PCPs). Design Cluster-randomized controlled trial. Setting Primary care practices in a large network of federally qualified health centers. Participants Sixteen PCPs block randomized to Nutri (n = 8) or control (n =8) and 30 of their adult diabetes/prediabetes patients (Nutri, n = 17; control, n=13). Intervention(s) After patients completed the automated self-administered 24-hour dietary assessment tool, Nutri synthesized diet data to prioritize dietary problems and guide PCPs through collaborative diet goal setting during a regularly scheduled appointment. Control PCPs provided usual care. Main Outcome Measure(s) Completion rates (trial feasibility); Nutri usage, usability (intervention feasibility); patient-reported goal setting, self-efficacy, diet quality; PCP-reported diet counseling self-efficacy, attitudes, and competence. Analysis Summary of trial and intervention feasibility; intent-to-treat comparisons with Bayesian mixed effects models (patient outcomes accounting for PCP-level clustering), ordinary least squares regression (PCP outcomes). Results All PCPs and patients matriculated into the trial were followed through posttest. Nutri PCPs used Nutri in all study appointments; 81% of Nutri patients reported goal setting, and 57% initiated their goal. PCP-reported diet counseling self-efficacy and competence improved. Conclusions and Implications Nutri was useful to PCPs for collaborative diet goal setting with the potential to improve diabetes management in safety-net clinics.

The recently developed semi-parametric generalized linear model (SPGLM) offers more flexibility as compared to the classical GLM by including the baseline or reference distribution of the response as an additional parameter in the model. However, some inference summaries are not easily generated under existing maximum-likelihood-based inference (GLDRM). This includes uncertainty in estimation for model-derived functionals such as exceedance probabilities. The latter are critical in a clinical diagnostic or decision-making setting. In this article, by placing a Dirichlet prior on the baseline distribution, we propose a Bayesian model-based approach for inference to address these important gaps. We establish consistency and asymptotic normality results for the implied canonical parameter. Simulation studies and an illustration with data from an aging research study confirm that the proposed method performs comparably or better in comparison with GLDRM. The proposed Bayesian framework is most attractive for inference with small sample training data or in sparse-data scenarios.

Early life trauma exposure is associated with substance use behavior during adolescence. The current study used a sample of trauma-exposed youth (ages 12-20, N = 2391) to determine if lifetime exposure to interpersonal or incidental trauma was associated with clinical diagnoses of substance use disorder across adolescence and whether the association was amplified by other risk factors. Interpersonal, but not incidental, trauma exposure conferred greater risk for substance use disorder diagnoses. Psychiatric comorbidity and family history of substance abuse were also identified as risk factors for adolescent substance use disorders. Associations between trauma and substance use disorder were constant throughout adolescence. These findings highlight interpersonal trauma exposure as a robust predictor of substance use disorders throughout adolescence.

Decision aids (DAs) may increase engagement in decision-making by addressing barriers that disproportionately impact socioeconomically disadvantaged patients. The impact of a breast cancer surgery DA on increasing patient engagement in decision-making was tested in clinics serving a high proportion of socioeconomically disadvantaged patients. A stepped-wedge trial was conducted with 10 National Cancer Institute Community Oncology Research Program clinics (Alliance for Clinical Trials in Oncology, June 2019 to December 2021). The clinics were randomized to time of transition from usual care (UC) to delivery of a web-based DA. Patients with stages 0–3 breast cancer eligible for surgery provided consent before a surgical consultation. Engagement was measured by Patient’s Self-Efficacy in Patient-Physician Interactions (PEPPI-5, follow-up survey) and count of Active Patient Behaviors (audio-recorded consultation). Intervention effects were tested with linear mixed-effects models, accounting for surgeon and clinic-level clustering, time, and enrollment after COVID. Heterogeneity of treatment effect by socioeconomic disadvantage (using the Area Deprivation Index) was assessed with an interaction term. The study enrolled 576 patients, and 44

Power and sample size calculations for Wald tests in generalized linear models (GLMs) are often limited to specific cases like logistic regression. More general methods typically require detailed study parameters that are difficult to obtain during planning. We introduce two new effect size measures for estimating power, sample size, or the minimally detectable effect size in studies using Wald tests across any GLM. These measures accommodate any number of predictors or adjusters and require only basic study information. We provide practical guidance for interpreting and applying these measures to approximate a key parameter in power calculations. We also derive asymptotic bounds on the relative error of these approximations, showing that accuracy depends on features of the GLM such as the nonlinearity of the link function. To complement this analysis, we conduct simulation studies across common model specifications, identifying best use cases and opportunities for improvement. Finally, we test the methods in finite samples to confirm their practical utility.

We introduce a novel varying-weight dependent Dirichlet process (DDP) model that extends a recently developed semi-parametric generalized linear model (SPGLM) by adding a nonparametric Bayesian prior on the baseline distribution of the GLM. We show that the resulting model takes the form of an inhomogeneous completely random measure that arises from exponential tilting of a normalized completely random measure. Building on familiar posterior sampling methods for mixtures with respect to normalized random measures, we introduce posterior simulation in the resulting model. We validate the proposed methodology through extensive simulation studies and illustrate its application using data from a speech intelligibility study.

PURPOSE:We sought to characterize fatigue of adults when listening to speech of children with cerebral palsy (CP). METHOD:Fifty-seven children with CP (19 without dysarthria and 38 with dysarthria) produced single-word and multiword speech samples. One hundred fourteen adult listeners completed transcription intelligibility tasks and provided listening fatigue ratings. Multiword utterances were analyzed in terms of speech rate and communication efficiency. RESULTS:Intraclass correlations showed large individual differences for listening fatigue ratings. Pearson correlations showed negative relationships between listening fatigue and intelligibility; however, the magnitude varied depending upon utterance length and dysarthria status of child speakers. Pearson correlations between listening fatigue and speech rate and between listening fatigue and communication efficiency varied depending upon dysarthria status of child speakers. Welch's t test showed that listeners of children with dysarthria had higher fatigue ratings than listeners of children without dysarthria. Listeners of children with dysarthria were more fatigued following multiword utterances than single-word utterances. Best subset regression showed that the combined effect of dysarthria status, intelligibility, and speech rate best explained listening fatigue of adult listeners. CONCLUSIONS:Listeners had increased levels of fatigue when they heard dysarthric speech relative to nondysarthric speech. The needs of both speaker and listener should be considered when supporting children with CP and dysarthria to achieve successful communication.

PURPOSE:The goal of the present study was to quantify the relationship between Intelligibility in Context Scale (ICS) scores and transcription intelligibility scores of typically developing English-speaking children when the confounding effects of age were controlled. METHOD:Five hundred forty-five typically developing children aged 2;6 to 9;11 (years;months) participated in the study. Parents of each child participant completed the ICS. Naïve listeners orthographically transcribed speech samples from child participants. To control the effect of age, we computed Spearman correlation values between the transcription intelligibility scores and ICS scores stratified by age bands. To estimate the relationship between the transcription intelligibility score and ICS scores across age while controlling the effect of age, we averaged Spearman correlation values across age bands. RESULTS:Spearman correlations between ICS scores (as composite scores or as individual ICS item scores) and transcription intelligibility scores were generally negligible to low, indicating a weak relationship. CONCLUSIONS:ICS and transcription intelligibility are complementary measures in giving a holistic picture of how well a child is understood, in shaping an individualized interventional plan and in monitoring progress. SUPPLEMENTAL MATERIAL:https://doi.org/10.23641/asha.29856173.

A major obstacle hindering the broad adoption of polygenic scores (PGS) is their lack of "portability" to people that differ-in genetic ancestry or other characteristics-from the GWAS samples in which genetic effects were estimated. Here, we use the UK Biobank to measure the change in PGS prediction accuracy as a continuous function of individuals' genome-wide genetic dissimilarity to the GWAS sample ("genetic distance"). Our results highlight three gaps in our understanding of PGS portability. First, prediction accuracy is extremely noisy at the individual level and not well predicted by genetic distance. In fact, variance in prediction accuracy is explained comparably well by socioeconomic measures. Second, trends of portability vary across traits. For several immunity-related traits, prediction accuracy drops near zero quickly even at intermediate levels of genetic distance. This quick drop may reflect GWAS associations being more ancestry-specific in immunity-related traits than in other traits. Third, we show that even qualitative trends of portability can de pend on the measure of prediction accuracy used. For instance, for type 2 diabetes, precision stays roughly constant, while recall surprisingly increases, with genetic distance. Together, our results show that portability cannot be understood through global ancestry groupings alone. There are other, understudied factors influencing portability, such as the specifics of the evolution of the trait and its genetic architecture, social context, and the construction of the polygenic score. Addressing these gaps can aid in the development and application of PGS and inform more equitable genomic research.

Approximately 2 % of preadolescents in the United States have Major Depressive Disorder (MDD) which is associated with long-term morbidity; however, they are frequently underrecognized and undertreated. Guided by the need for self-report depression screening instruments in children below 13 years of age, this study sought to establish the psychometric properties of the Patient Health Questionnaire for Adolescents (PHQ-9A) as a dimensional measure and obtain a categorical cut-point using a structured interview-based diagnosis of current MDD in preadolescents. Preadolescent participants (10-12-year-olds; n = 470) were drawn from the Texas Childhood Trauma Research Network, a longitudinal registry of youth with trauma exposure. Diagnoses were assessed with the Mini-International Neuropsychiatric Interview for Children and Adolescents. Self-reported assessments included PHQ-9A and other psychosocial scales. The two-factor model separating cognitive/affective and somatic symptoms showed a good fit. High correlations (0.84) between factors and strong internal reliability (α = 0.83) support the use of a total score. Test-retest latent variable correlations were 0.68 over one month. Fit was invariant for sex and age (compared to 13-14-year-olds). PHQ-9A scores were correlated with related constructs. At the Youden Index optimal cut-point of 5, sensitivity was 0.93 and specificity was 0.60, against MDD diagnosis. Those above this cut-point who did not get an MDD diagnosis had significant impairment with indicators of high risk for future MDD. Alternative cut-points with different ratios for specificity and sensitivity are offered. Our study supports the PHQ-9A as a brief and free screening tool for preadolescent youth in clinical and research settings.

Childhood trauma exposure is associated with posttraumatic stress symptoms (PTSS) and suicidality, however it is also a risk factor for obsessive-compulsive disorder (OCD) in adults. Research examining the relationship between childhood trauma and OCD in youth is mixed, and there is a dearth of research examining the associations among OCD, PTSS, and suicidality. As a result, conclusions have been drawn from primarily cross-sectional adult samples. No study has examined the clinical characteristics associated with OCD in trauma-exposed youth, nor its associations with PTSS and suicidality over time. To address this gap, the present study used logistic regressions and generalized estimating equations in 2068 trauma-exposed youth aged 8—20 who completed assessments at baseline, 6-month, and 12-month follow-ups. In total, trauma-exposed youth with OCD (n = 222, 10.7 %) were more likely to be female (OR = 0.646), had more severe PTSS (OR = 1.032), and more psychiatric comorbidities (OR = 1.391) compared to trauma-exposed youth without OCD. Interpersonal traumas (OR = 1.549) and bullying (OR = 1.294) were associated with a greater likelihood of having OCD; however, these effects were nonsignificant when adjusting for other mental health symptoms. There was no evidence that OCD was associated with the trajectory of PTSS nor suicidality at 6- and 12-month follow-ups. Trauma-exposed youth with OCD may cross-sectionally have more severe clinical presentations overall, but OCD may not be related to the trajectory of these symptoms over time. Future research is needed to understand the directionality of clinical characteristics associated with pediatric OCD and whether interpersonal traumas convey risk uniquely for OCD or for distress in general.

INTRODUCTION:Cognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, yet sensitive, tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from clinic staff. The purpose of this project was (1) to develop an automated cognitive screening tool incorporating cognitive and speech/language data using machine learning techniques for potential use in primary care settings and (2) to compare its classification accuracy to an established cognitive screening measure. METHODS:Participants were 53 cognitively normal and 51 cognitively impaired older adults. Each completed a working memory (WM) and four speaking tasks, followed by a second administration of WM to investigate the added utility of practice effects. Bayesian additive regression trees were used to test nine models, and the Quick Mild Cognitive Impairment screen was administered as a comparator. RESULTS:The top feature set consisted of both administrations of the WM task and a personal narrative task and achieved a cross-validated classification accuracy (area under the receiver operating characteristics curve) of 0.84, which was slightly better than the comparator. DISCUSSION:Combining WM and acoustic and linguistic variables derived from connected speaking tasks discriminated cognitively normal from cognitively impaired groups with a high degree of accuracy. Highlights:Working memory and speaking tasks were used for detection of cognitive impairment.This combination distinguished cognitively normal from impaired older adults.This automated tool may overcome barriers to cognitive screening in primary care.

Abstract Background Asthma-related emergency department (ED) visit rates among children in a typical year closely follow the school calendar, with a sharp increase in rates when students return to school. The well-known increase in asthma exacerbations at the start of the school year is largely attributable to increases in respiratory viral infection. Because neighborhood disadvantage is strongly tied to asthma exacerbations, we hypothesized that greater neighborhood disadvantage would be associated with a greater increase in the rate of asthma exacerbations after the start of school. Methods We summarized Texas Health Care Information Collective data (2016-2019) on asthma-related ED visits among children (ages 5-17) in four Texas metropolitan statistical areas (Houston, Dallas, Austin, and San Antonio) by week and census tract. Neighborhood disadvantage was characterized at the census-tract level using tertiles of Social Vulnerability Index (SVI), percent uninsured, and percent living below the federal poverty line. Within each tertile, we described population-based incidence rates of asthma-related ED visits per 100,000 person-years in the 6 weeks before and after the week that school started. We also modeled weekly incidence rates and pointwise 95% confidence intervals by week using a locally weighted smoothing model. Results Comparing the 6-week periods before and after the week that school started, the incidence rate of asthma-related ED visits in high-, medium-, and low-SVI census tracts increased by 60, 39, and 20 cases per 100,000 person-years, respectively, representing a 3-fold difference between high- and low-SVI census tracts. Census tracts with high SVI had the greatest modeled increase in weekly rates of asthma-related ED visits after the start of school (Figure 1). Results were qualitatively similar for the percent of the population uninsured and below the federal poverty line (Figures 2-3). Conclusion Greater neighborhood disadvantage was associated with a greater increase in the rate of asthma related ED visits after the start of school, suggesting that neighborhood disadvantage may contribute to exacerbation risk by increasing the risk of respiratory virus infection. Disclosures Paul Rathouz, PhD, Sunovion Pharmaceuticals: DSMB Member

Background:Patient decision aids (DAs) improve decision quality during shared decision-making (SDM) for patients seeking care for knee osteoarthritis (OA). However, few DAs incorporate the 'digital twin' concept where comprehensive data are applied to computational models to generate dynamic virtual simulations and predictions to augment decision-making in real-time. We developed an artificial intelligence-enabled DA (AI-DA) that generated digital twins using patient reported outcome measurements (PROMs) and clinical data to enhance SDM by providing personalized predictions of risks and benefits for patients with knee OA considering total knee arthroplasty (TKA). We assessed the impact of the AI-DA on patient- and process-level outcomes. Methods:We performed a randomized open-label clinical trial at a single, university-affiliated orthopaedic clinic in the USA involving patients with knee OA between February 2021 and November 2022. Patients received a full AI-DA incorporating patient education, preference assessment, and person-specific benefit:risk predictions of TKA (intervention group) or patient education only (control group). Outcomes included the Knee OA Decision Quality Instrument (K-DQI) (primary outcome), CollaboRATE SDM survey, Decision Conflict Scale (DCS), Decision Regret Scale (DRS), and Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) for knee-specific health at 3 and 6 months post-randomization, satisfaction, appointment duration, and TKA rates. This study is registered with ClinicalTrials.gov, NCT04805554. Findings:The analytic sample comprised 101 patients ([mean [SD], 64.9 [10.1] years; 54 [54%] women]) in the intervention group and 100 patients (mean [SD] age 63.4 [8] years; 60 [60%] women) in the control group. The intervention group reported higher decision quality (mean [SD] K-DQI: 84.4 [25.2] versus 71.4 [29.8], P = 0.0011), lower decision conflict (DCS: 1.0 [3.1] versus 3.3 [5.8], P = 0.0029), lower decision regret at 6-9 months (DRS: 18.2 [19.5] versus 27.2 [24.2], P = 0.0051), better knee-specific health (KOOS JR: 69.5 [17.3] versus 47 [18.4], P < 0.0001) at 6-9 months, and greater treatment concordance (91% versus 76%, P = 0.0043). SDM scores, knee health at 3 months, patient and clinician satisfaction, appointment duration, TKA rates, and decision regret at 3 months were similar between groups. Interpretation:AI-DAs provide a more personalized, data-augmented SDM experience that can improve decision quality and longer-term health-related outcomes in patients with knee OA considering TKA. Funding:Agency for Healthcare Research and Quality Grant (R21HS027037).

Comorbidity between post-traumatic stress disorder (PTSD) and substance use disorder may be explained by a prospective trauma risk conferred by both conditions. The current study modeled concurrent and prospective associations of trauma, PTSD symptoms, and substance use (SU) behavior among trauma exposed youth (ages 8-20). Clinical interviews assessed trauma exposure, PTSD symptom severity, and SU behavior at baseline and at six- and 12-month follow up study visits (N = 2,069). Structural equation models assessed the associations of trauma, PTSD symptoms, and SU behavior. Lifetime trauma was associated with more severe PTSD symptoms and SU behaviors, whereas trauma exposure during the study was only associated with PTSD symptoms. PTSD symptom severity was prospectively associated with trauma exposure. PTSD symptom severity and SU behavior at follow-up study visits were prospectively associated. These results highlight the dynamic interplay between trauma, PTSD symptoms, and SU behavior during youth, a developmental period during which complex psychiatric presentations can have longstanding consequences for health.