A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.

BACKGROUND/AIMS: Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis. METHODS: Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016-2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion. RESULTS: We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1-Q3) 1426 (420-7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a "preliminary" evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis. CONCLUSION: Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness is required that the use of standard sample size calculation methods can provide spuriously low numbers of required clusters. Methods such as generalized estimating equations or generalized linear mixed models with small-sample corrections, Bayesian approaches, and permutation tests may be more appropriate for the analysis of small stepped-wedge cluster randomized trials. Future research is needed to establish best practices for stepped-wedge cluster randomized trials with a small number of clusters.

Negative Life Events and Emotional Symptoms From Ages 2 to 30 Years.

Importance: Exposure to different types of negative life events, including traumatic events, is common across the lifespan and associated with increased mental health symptoms. Objective: To assess whether vulnerability to negative life events varies across 5 developmental periods from preschool to young adulthood. Design, Setting, and Participants: This cohort study analyzed data from 3 community-representative studies set in the southeastern US (1992-2015) with harmonized assessment approaches that included a total of 13 775 assessments of individuals aged 2 to 30 years with up to 21 years of follow-up. Data analysis occurred from July 2023 to June 2024. Exposures: Each study assessed lifetime exposure to (1) traumatic events (ie, severe events associated with posttraumatic stress disorder) and (2) recent stressful events (eg, loss of a friend or moving). All assessments were completed with structured interviews with participants and/or their caregivers. Main Outcomes and Measures: The primary outcome was emotional symptoms (ie, anxiety and depressive symptoms). Associations of both categories of life events with emotional symptoms were compared across preschool (<7 years), childhood (7-12 years), adolescence (13-17 years), late adolescence (18-22 years), and young adulthood (23-30 years). Results: Analyses were based on 13 775 assessments of 3258 participants (1519 female [weighted percentage, 50.0%]). Recent stressful events were associated with emotional symptoms across each developmental period, ranging from a low in preschool (B =0.14; SE = 0.05) to a high in young adulthood (B = 0.57; SE = 0.12) in cross-sectional analyses and ranging from a low in childhood (B = 0.10; SE = 0.06) to a high adolescence (B = 0.19; SE = 0.05) in longitudinal analyses. Lifetime traumatic events were associated with emotional symptoms across each developmental period, ranging from a low in preschool (B = 0.18; SE = 0.05) to a high in adolescence (B = 0.28; SE = 0.04) in cross-sectional analyses and ranging from a low in childhood (B = 0.09; SE = 0.06) to a high in late adolescence (B = 0.21; SE = 0.05) in longitudinal analyses. Associations had overlapping 95% CIs across the different developmental periods with one exception: stressful events had a larger-magnitude cross-sectional association with emotional symptoms in young adulthood than in other developmental periods. Results were consistent with additive, rather than interactive, associations of traumatic and stressful events with emotional symptoms at each developmental period. Conclusions and Relevance: In this cohort study of 3 community-representative samples, vulnerability to traumatic and stressful events was generally similar across the first 3 decades of life; both types of events had an independent association with emotional functioning. These findings suggest response to stressful events is similar from childhood to adulthood.

HIV Symptom Clusters Among Sexual Minority Men in Ghana, West Africa: A Cross-sectional Study.

ABSTRACT: The aims of this study were to determine if HIV symptoms among sexual minority men formed clusters and to examine the sociodemographic and clinical characteristics that are associated with these clusters. We analyzed cross-sectional data from Ghanaian sexual minority men (N = 225) living with HIV. We used both principal component analysis and multivariable linear regression. Our findings indicate that sadness (64.0%) and headache (62.7%) were the most prevalent symptoms among our sample. Seven symptom clusters were identified: neurological symptoms, psychological symptoms, gastrointestinal symptoms, dermatological symptoms, self-concept/self-esteem, weight/diet-related symptoms, and sleepquality and potential disturbances. Late HIV diagnosis was significantly associated with higher distress scores for all symptom clusters except for the self-concept/self-esteem and gastrointestinal symptoms clusters. The findings emphasize the importance of early HIV symptom identification.

Correction: Parental supervision positively impacts children's economic prospects two decades later: A prospective longitudinal study.

[This corrects the article DOI: 10.1371/journal.pone.0286218.].

Sociodemographic correlates of late HIV diagnosis among men who have sex with men (MSM) in Ghana.

Timely HIV diagnosis and medical engagement are crucial for effective viral load suppression and treatment as prevention. However, significant delays persist, particularly in Africa, including Ghana. This study focused on Ghanaian men whose route of exposure to HIV was through same-gender sexual contact (MSM), a group disproportionately impacted by HIV. Using structured surveys, we investigated the sociodemographic factors associated with late HIV diagnosis, a topic with limited existing research. Results indicate that older age groups were associated with an increased risk of late diagnosis compared to the 18-24 age group. Among the demographic variables studied, only age showed a consistent association with late HIV diagnosis. This study underscores the importance of targeted interventions to address HIV diagnosis disparities among MSM in Ghana, particularly for older age groups. The findings emphasize the need for tailored interventions addressing age-related disparities in timely diagnosis and engagement with medical services among this population. Such interventions can play a crucial role in reducing the burden of HIV within this community and fostering improved public health outcomes.

A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.

Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and degree of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field.

Trends in Firearm Injury Prevention Research Funding, Clinical Trials, and Publications in the US, 1985-2022.

This cross-sectional study examines federal funding, registered clinical trials, and publications to quantify trends in firearm injury prevention research in the US from 1985 to 2022.

Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.

BACKGROUND: Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes. METHODS: This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings. RESULTS: Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity. CONCLUSION: We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.

Designing multicenter individually randomized group treatment trials.

In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between arms within the same center. While the design of a single-center IRGT trial has been studied, little is known about the planning of a multicenter IRGT trial. To address this gap, this paper provides analytical sample size formulas for designing multicenter IRGT trials with a continuous endpoint under the linear mixed model framework. We found that accounting for the additional center-level correlation at the design stage can lead to sample size reduction, and the magnitude of reduction depends on the amount of between-therapist correlation. However, if the variance components of therapist-level random effects are considered as input parameters in the design stage, accounting for the additional center-level variance component has no impact on the sample size estimation. We presented our findings through numeric illustrations and performed simulation studies to validate our sample size procedures under different scenarios. Optimal design configurations under the multicenter IRGT trials have also been discussed, and two real-world trial examples are drawn to illustrate the use of our method.

Intergenerational Effects of a Family Cash Transfer on the Home Environment.

OBJECTIVE: A natural experiment that provided income supplements to families has been associated with beneficial outcomes for children that persisted into adulthood. The children in this study are now adults, and many are parents. METHOD: The study builds on the longitudinal, representative Great Smoky Mountains study conducted from 1993 to 2020. At follow-up in their late 30s, 1,094 of the 1,348 living participants (81.2%) were assessed. Of these participants (67.6%), 739 were parents. A tribe in the area implemented a cash transfer program of approximately $5,000 annually per person to every tribal member based on the profits received from operating a casino. Ten aspects of the home environment of participants were assessed (eg, family chaos, substance use, and food insecurity) as well as a composite measure across all home environment indicators. The proposed analyses were preregistered (https://osf.io/ex638). RESULTS: Of the 739 parents assessed, 192 (26.0%) were American Indians. Parents whose families received cash transfers during childhood did not differ from parents whose families did not receive cash transfers on any of the home environment indicators or the composite measure. At the same time, there was little evidence of elevated risk for participants in either group in measures of parental mental health, substance use, and violence. CONCLUSION: A family cash transfer in childhood that had long-term effects on individual functioning did not impact the home environment of participants who became parents. Rather, parents in both groups were providing home environments generally conducive to their children's growth and development. STUDY PREREGISTRATION INFORMATION: Intergenerational Effects of a Family Cash Transfer on the Home Environment; https://osf.io/; ex638.

A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials.

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016-2022.

BACKGROUND/AIMS: The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor. METHODS: We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion. RESULTS: We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation. CONCLUSIONS: Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.

Violent Experiences and Patterns of Firearm Ownership From Childhood to Young Adulthood.

Importance: Young adults in their 20s are at high relative risk for self- and other-directed firearm injury, but little is known about gun access patterns for this group. Objective: To describe the longitudinal patterns of firearm access from childhood to young adulthood and to estimate whether violence experienced as a child or as an adult is associated with gun ownership in young adulthood. Design, Setting, and Participants: The Great Smoky Mountains Study included participants from 11 contiguous, mostly rural counties in the Southeastern US. The first wave was completed in 1993 and the most recent in 2019. Periodic survey data were gathered in adolescence through participants' late 20s. In 2023, adjusted Poisson regression with incident rate ratios (IRRs) and 95% CIs were used to estimate associations between violence and gun ownership in young adulthood in 3 age cohorts from the original sample. Exposures: Violent experiences in childhood (bullying, sexual and physical abuse, violent events, witnessing trauma, physical violence between parents, and school/neighborhood dangerousness) or adulthood (physical and sexual assault). Main Outcomes and Measures: Initiating gun ownership was defined as no gun access or ownership in childhood followed by gun ownership at age 25 or 30 years. Maintaining gun ownership was defined as reporting gun access or ownership in at least 1 survey in childhood and ownership at age 25 or 30 years. Results: Among 1260 participants (679 [54%] male; ages 9, 11, and 13 years), gun access or ownership was more common in childhood (women: 366 [63%]; men: 517 [76%]) than in adulthood (women: 207 [36%]; men: 370 [54%]). The most common longitudinal pattern was consistent access or ownership from childhood to adulthood (373 [35%]) followed by having access or ownership in childhood only (408 [32%]). Most of the violent exposures evaluated were not significantly associated with the outcomes. Being bullied at school was common and was associated with reduced ownership initiation (IRR, 0.76; 95% CI, 0.61-0.94). Witnessing a violent event was significantly associated with increased probability of becoming a gun owner in adulthood (IRR, 1.24; 95% CI, 1.03-1.49). Conclusions and Relevance: In this cohort study, gun ownership and access were transitory, even in a geographic area where gun culture is strong. Early adulthood-when the prevalence of gun ownership was relatively low-may represent an opportune time for clinicians and communities to provide education on the risks associated with firearm access, as well as strategies for risk mitigation.

Trusted residents and housing assistance to decrease violence exposure in New Haven (TRUE HAVEN): a strengths-based and community-driven stepped-wedge intervention to reduce gun violence.

BACKGROUND: We describe the rationale and study design for "TRUsted rEsidents and Housing Assistance to decrease Violence Exposure in New Haven (TRUE HAVEN)," a prospective type 1 hybrid effectiveness/implementation study of a multi-level intervention using a stepped wedge design. TRUE HAVEN aims to lower rates of community gun violence by fostering the stability, wealth, and well-being of individuals and families directly impacted by incarceration through the provision of stable housing and by breaking the cycle of trauma. DESIGN: TRUE HAVEN is an ongoing, multi-level intervention with three primary components: financial education paired with housing support (individual level), trauma-informed counseling (neighborhood level), and policy changes to address structural racism (city/state level). Six neighborhoods with among the highest rates of gun violence in New Haven, Connecticut, will receive the individual and neighborhood level intervention components sequentially beginning at staggered 6-month steps. Residents of these neighborhoods will be eligible to participate in the housing stability and financial education component if they were recently incarcerated or are family members of currently incarcerated people; participants will receive intense financial education and follow-up for six months and be eligible for special down payment and rental assistance programs. In addition, trusted community members and organization leaders within each target neighborhood will participate in trauma-informed care training sessions to then be able to recognize when their peers are suffering from trauma symptoms, to support these affected peers, and to destigmatize accessing professional mental health services and connect them to these services when needed. Finally, a multi-stakeholder coalition will be convened to address policies that act as barriers to housing stability or accessing mental healthcare. Interventions will be delivered through existing partnerships with community-based organizations and networks. The primary outcome is neighborhood rate of incident gun violence. To inform future implementation and optimize the intervention package as the study progresses, we will use the Learn As You Go approach to optimize and assess the effectiveness of the intervention package on the primary study outcome. DISCUSSION: Results from this protocol will yield novel evidence for whether and how addressing structural racism citywide leads to a reduction in gun violence. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05723614. Registration date: February 01, 2023. Please refer to https://clinicaltrials.gov/ct2/show/NCT05723614 for public and scientific inquiries.

Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances.

An important consideration in the design and analysis of randomized trials is the need to account for outcome observations being positively correlated within groups or clusters. Two notable types of designs with this consideration are individually randomized group treatment trials and cluster randomized trials. While sample size methods for testing the average treatment effect are available for both types of designs, methods for detecting treatment effect modification are relatively limited. In this article, we present new sample size formulas for testing treatment effect modification based on either a univariate or multivariate effect modifier in both individually randomized group treatment and cluster randomized trials with a continuous outcome but any types of effect modifier, while accounting for differences across study arms in the outcome variance, outcome intracluster correlation coefficient (ICC) and the cluster size. We consider cases where the effect modifier can be measured at either the individual level or cluster level, and with a univariate effect modifier, our closed-form sample size expressions provide insights into the optimal allocation of groups or clusters to maximize design efficiency. Overall, our results show that the required sample size for testing treatment effect heterogeneity with an individual-level effect modifier can be affected by unequal ICCs and variances between arms, and accounting for such between-arm heterogeneity can lead to more accurate sample size determination. We use simulations to validate our sample size formulas and illustrate their application in the context of two real trials: an individually randomized group treatment trial (the AWARE study) and a cluster randomized trial (the K-DPP study).

Cultural contributions to adults' self-rated mental health problems and strengths: 7 culture clusters, 28 societies, 16 906 adults.

BACKGROUND: It is unknown how much variation in adult mental health problems is associated with differences between societal/cultural groups, over and above differences between individuals. METHODS: To test these relative contributions, a consortium of indigenous researchers collected Adult Self-Report (ASR) ratings from 16 906 18- to 59-year-olds in 28 societies that represented seven culture clusters identified in the Global Leadership and Organizational Behavioral Effectiveness study (e.g. Confucian, Anglo). The ASR is scored on 17 problem scales, plus a personal strengths scale. Hierarchical linear modeling estimated variance accounted for by individual differences (including measurement error), society, and culture cluster. Multi-level analyses of covariance tested age and gender effects. RESULTS: Across the 17 problem scales, the variance accounted for by individual differences ranged from 80.3% for DSM-oriented anxiety problems to 95.2% for DSM-oriented avoidant personality (mean = 90.7%); by society: 3.2% for DSM-oriented somatic problems to 8.0% for DSM-oriented anxiety problems (mean = 6.3%); and by culture cluster: 0.0% for DSM-oriented avoidant personality to 11.6% for DSM-oriented anxiety problems (mean = 3.0%). For strengths, individual differences accounted for 80.8% of variance, societal differences 10.5%, and cultural differences 8.7%. Age and gender had very small effects. CONCLUSIONS: Overall, adults' self-ratings of mental health problems and strengths were associated much more with individual differences than societal/cultural differences, although this varied across scales. These findings support cross-cultural use of standardized measures to assess mental health problems, but urge caution in assessment of personal strengths.

TRUsted rEsidents and Housing Assistance to decrease Violence Exposure in New Haven (TRUE HAVEN): A strengths-based and community-driven stepped-wedge intervention to reduce gun violence.

Background We describe the rationale and study design for " TRU sted r Esidents and H ousing A ssistance to decrease V iolence E xposure in N ew Haven (TRUE HAVEN)," a prospective type 1 hybrid effectiveness/implementation study of a multi-level intervention using a stepped wedge design. TRUE HAVEN aims to lower rates of community gun violence by fostering the stability, wealth, and well-being of individuals and families directly impacted by incarceration through the provision of stable housing and by breaking the cycle of trauma. Design: TRUE HAVEN is a multi-level intervention with three primary components: financial education paired with housing support (individual level), trauma-informed counseling (neighborhood level), and policy changes to address structural racism (city/state level). Six neighborhoods with among the highest rates of gun violence in New Haven, Connecticut, will receive the individual and neighborhood level intervention components sequentially beginning at staggered 6-month steps. Residents of these neighborhoods will be eligible to participate in the housing stability and financial education component if they were recently incarcerated or are family members of currently incarcerated people; participants will receive intense financial education and follow-up for six months and be eligible for special down payment and rental assistance programs. In addition, trusted community members and organization leaders within each target neighborhood will participate in trauma-informed care training sessions to then be able to recognize when their peers are suffering from trauma symptoms, to support these affected peers, and to destigmatize accessing professional mental health services and connect them to these services when needed. Finally, a multi-stakeholder coalition will be convened to address policies that act as barriers to housing stability or accessing mental healthcare. Interventions will be delivered through existing partnerships with community-based organizations and networks. The primary outcome is neighborhood rate of incident gun violence. To inform future implementation and optimize the intervention package as the study progresses, we will use the Learn As You Go approach to optimize and assess the effectiveness of the intervention package on the primary study outcome. Discussion Results from this protocol will yield novel evidence for whether and how addressing structural racism citywide leads to a reduction in gun violence. Trial registration ClinicalTrials.gov Identifier: NCT05723614. Registration date: February 01, 2023.

Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity.

BACKGROUND: Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown. METHODS: We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example. RESULTS: Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators. CONCLUSION: Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur.

A scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials.

OBJECTIVES: In stepped-wedge cluster randomized trials (SW-CRTs), clusters are randomized not to treatment and control arms but to sequences dictating the times of crossing from control to intervention conditions. Randomization is an essential feature of this design but application of standard methods to promote and report on balance at baseline is not straightforward. We aimed to describe current methods of randomization and reporting of balance at baseline in SW-CRTs. STUDY DESIGN AND SETTING: We used electronic searches to identify primary reports of SW-CRTs published between 2016 and 2022. RESULTS: Across 160 identified trials, the median number of clusters randomized was 11 (Q1-Q3: 8-18). Sixty-three (39%) used restricted randomization-most often stratification based on a single cluster-level covariate; 12 (19%) of these adjusted for the covariate(s) in the primary analysis. Overall, 50 (31%) and 134 (84%) reported on balance at baseline on cluster- and individual-level characteristics, respectively. Balance on individual-level characteristics was most often reported by condition in cross-sectional designs and by sequence in cohort designs. Authors reported baseline imbalances in 72 (45%) trials. CONCLUSION: SW-CRTs often randomize a small number of clusters using unrestricted allocation. Investigators need guidance on appropriate methods of randomization and assessment and reporting of balance at baseline.