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BACKGROUND: Germline genetic testing is recommended for an increasing number of conditions with underlying genetic etiologies, the results of which impact medical management. However, genetic testing is underutilized in clinics due to system, clinician, and patient level barriers. Behavioral economics provides a framework to create implementation strategies, such as nudges, to address these multi-level barriers and increase the uptake of genetic testing for conditions where the results impact medical management. METHODS: Patients meeting eligibility for germline genetic testing for a group of conditions will be identified using electronic phenotyping algorithms. A pragmatic, type 3 hybrid cluster randomization study will test nudges to patients and/or clinicians, or neither. Clinicians who receive nudges will be prompted to either refer their patient to genetics or order genetic testing themselves. We will use rapid cycle approaches informed by clinician and patient experiences, health equity, and behavioral economics to optimize these nudges before trial initiation. The primary implementation outcome is uptake of germline genetic testing for the pre-selected health conditions. Patient data collected through the electronic health record (e.g. demographics, geocoded address) will be examined as moderators of the effect of nudges. DISCUSSION: This study will be one of the first randomized trials to examine the effects of patient- and clinician-directed nudges informed by behavioral economics on uptake of genetic testing. The pragmatic design will facilitate a large and diverse patient sample, allow for the assessment of genetic testing uptake, and provide comparison of the effect of different nudge combinations. This trial also involves optimization of patient identification, test selection, ordering, and result reporting in an electronic health record-based infrastructure to further address clinician-level barriers to utilizing genomic medicine. The findings may help determine the impact of low-cost, sustainable implementation strategies that can be integrated into health care systems to improve the use of genomic medicine. TRIAL REGISTRATION: ClinicalTrials.gov. NCT06377033. Registered on March 31, 2024. https://clinicaltrials.gov/study/NCT06377033?term=NCT06377033&rank=1.

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We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

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Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impacts of imbalanced baseline prognostic factors and varying cluster sizes between groups when these stratification factors are adopted in the design. Failure to account for stratification and cluster size variability may lead to underpowered analysis and inaccurate sample size estimation. Apart from the sample size estimation in unstratified cluster randomization trials, there are no development of an explicit sample size formula for survival endpoint when a stratified cluster randomization design is employed. In this article, we present a closed-form sample size formula based on the stratified cluster log-rank statistics for stratified cluster randomization trials with survival endpoint. It provides an integrated solution for sample size estimation that account for cluster size variation, baseline hazard heterogeneity, and the estimated intracluster correlation coefficient based on the preliminary data. Simulation studies show that the proposed formula provides the appropriate sample size for achieving the desired statistical power under various parameter configurations. A real example of a stratified cluster randomization trial in the population with stable coronary heart disease is presented to illustrate our method.

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BACKGROUND: Providing comprehensive and continuous care for patients whose conditions have mental or behavioral components is a central challenge in primary care and an important part of improving universal health coverage. There is a great need for high and routine availability of psychological interventions, but traditional methods for delivering psychotherapy often result in low reach and long wait times. Primary Care Behavioral Health (PCBH) is a method for organizing primary care in which behavioral health staff provide brief, flexible interventions to a large part of the population in active collaboration with other providers. While PCBH holds promise in addressing important challenges, it has not yet been thoroughly evaluated. METHODS: This cluster randomized trial will assess 17 primary care centers (PCCs) that are starting a PCBH implementation process. The PCCs will be divided into two groups, with one starting immediate implementation and the other acting as a control, implementing six months later. The purpose of the study is to strengthen the evidence base for PCBH regarding implementation-, organization-, and patient-level outcomes, taking into consideration that there is a partially dependent relationship between the three levels. Patient outcomes (such as increased daily functioning and reduction of symptoms) may be dependent on organizational changes (such as availability of treatment, waiting times and interprofessional teamwork), which in turn requires change in implementation outcomes (most notably, model fidelity). In addition to the main analysis, five secondary analyses will compare groups based on different combinations of randomization and time periods, specifically before and after each center achieves sufficient PCBH fidelity. DISCUSSION: A randomized comparison of PCBH and traditional primary care has, to our knowledge, not been made before. While the naturalistic setting and the intricacies of implementation pose certain challenges, we have designed this study in an effort to evaluate the causal effects of PCBH despite these complex aspects. The results of this project will be helpful in guiding decisions on how to organize the delivery of behavioral interventions and psychological treatment within the context of primary care in Sweden and elsewhere. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05335382. Retrospectively registered on March 13th, 2022.

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Longitudinal cluster-randomized designs have been popular tools for comparative effective research in clinical trials. The methodologies for the three-level hierarchical design with longitudinal outcomes need to be better understood under more pragmatic settings; that is, with a small number of clusters, heterogeneous cluster sizes, and missing outcomes. Generalized estimating equations (GEEs) have been frequently used when the distribution of data and the correlation model are unknown. Standard GEEs lead to bias and an inflated type I error rate due to the small number of available clinics and non-completely random missing data in longitudinal outcomes. We evaluate the performance of inverse probability weighted (IPW) estimating equations, with and without augmentation, for two types of missing data in continuous outcomes and individual-level treatment allocation mechanisms combined with two bias-corrected variance estimators. Our intensive simulation results suggest that the proposed augmented IPW method with bias-corrected variance estimation successfully prevents the inflation of false positive findings and improves efficiency when the number of clinics is small, with moderate to severe missing outcomes. Our findings are expected to aid researchers in choosing appropriate analysis methods for three-level longitudinal cluster-randomized designs. The proposed approaches were applied to analyze data from a longitudinal cluster-randomized clinical trial involving adults with serious mental illnesses.

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Not only do cluster randomized trials require a larger sample size than individually randomized trials, they also face many additional complexities. The potential for contamination is the most commonly used justification for using cluster randomization, but the risk of contamination should be carefully weighed against the more serious problem of questionable scientific validity in settings with post-randomization identification or recruitment of participants unblinded to the treatment allocation. In this paper we provide some simple guidelines to help researchers conduct cluster trials in a way that minimizes potential biases and maximizes statistical efficiency. The overarching theme of this guidance is that methods that apply to individually randomized trials rarely apply to cluster randomized trials. We recommend that cluster randomization be only used when necessary-balancing the benefits of cluster randomization with its increased risks of bias and increased sample size. Researchers should also randomize at the lowest possible level-balancing the risks of contamination with ensuring an adequate number of randomization units-as well as exploring other options for statistically efficient designs. Clustering should always be allowed for in the sample size calculation; and the use of restricted randomization (and adjustment in the analysis for covariates used in the randomization) should be considered. Where possible, participants should be recruited before randomizing clusters and, when recruiting (or identifying) participants post-randomization, recruiters should be masked to the allocation. In the analysis, the target of inference should align with the research question, and adjustment for clustering and small sample corrections should be used when the trial includes less than about 40 clusters.

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Introduction: Adding developmental networks (DN) to grant-writing coaching can significantly enhance ESIs' research careers. Herein, we present study design, ESIs' characteristics and encountered challenges/lessons learned and their resolutions when deploying/implementing (a) NCR algorithm(s), (b) recruitment/retention and (c) implementing DN intervention. Methods: Nested Cluster Randomization (NCR) design governs this study implementation. The sample size is 220 ESIs intending to submit an NIH K, R, U, and/or Minority Supplement application(s). Primary outcome: intensity/sustainability of grant submission(s)/funding(s), measured by time to/between application(s). Outcome(s) analyses modes: summaries, Kaplan Meir and Cox proportional hazard models as a function of randomization groups and other predictors of outcomes. Results: In the present study, we recruited two cohorts of ESIs (N = 85): 39% African Americans, 18% Latinx, 18% Whites, 20% Asians and 6% Hawaiian/Pacific Islander/other ethnicities; 65% are women; 73% are assistant professors, 4% are Associate Professors and 23% are instructors/scientists/post-doctoral. Participants' disciplines: 32% basic/biomedical, 36% clinical/translational and 32% social/behavioral. Proposal(s) mechanisms: 61% research grants (R series), 31% career development (K series), 7% support of competitive research (SCORE) and 1% National Science Foundation applications. NCR did produce balance in the distribution of ESIs' demographics, sex at birth, ethnicity, professional appointments, background disciplines, and mechanism of sought funding. Lessons learned/challenges: NCR implementation was methodologically challenged during implementation by added constraints (e.g., assigning coaches to the same randomization arm of their participants as well as blinding them to ESIs' randomization group). Recruitment and retention were hampered by the COVID-19 pandemic and more progressive and innovative strategies were needed to heighten the visibility and outreach of this program. DN delivery was also affected by the pandemic and monitoring of ESIs' engagement and facilitation of communications interventions were needed. Resolution of these challenges effectively reconfigured NCR algorithms, recruitment/retention plans, and DN intervention delivery. We intend to recruit an additional 135 ESIs focusing on underrepresented scholars from RCMIs, CTSAs, and other programs. COVID-19 rendered this program 100% virtual, with recruitment/retention challenges and substantial disruption of ESIs' research. We may extend the grant writing period, coaching, and Mock Study Section support.

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BACKGROUND: The burden of stillbirth, neonatal and maternal deaths are unacceptably high in low- and middle-income countries, especially around the time of birth. There are scarce resources and/or support implementation of evidence-based training programs. SaferBirths Bundle of Care is a well-proven package of innovative tools coupled with data-driven on-the-job training aimed at reducing perinatal and maternal deaths. The aim of this project is to determine the effect of scaling up the bundle on improving quality of intrapartum care and perinatal survival. METHODS: The project will follow a stepped-wedge cluster implementation design with well-established infrastructures for data collection, management, and analysis in 30 public health facilities in regions in Tanzania. Healthcare workers from selected health facilities will be trained in basic neonatal resuscitation, essential newborn care and essential maternal care. Foetal heart rate monitors (Moyo), neonatal heart rate monitors (NeoBeat) and skills trainers (NeoNatalie Live) will be introduced in the health facilities to facilitate timely identification of foetal distress during labour and improve neonatal resuscitation, respectively. Heart rate signal-data will be automatically collected by Moyo and NeoBeat, and newborn resuscitation training by NeoNatalie Live. Given an average of 4000 baby-mother pairs per year per health facility giving an estimate of 240,000 baby-mother pairs for a 2-years duration, 25% reduction in perinatal mortality at a two-sided significance level of 5%, intracluster correlation coefficient (ICC) to be 0.0013, the study power stands at 0.99. DISCUSSION: Previous reports from small-scale Safer Births Bundle implementation studies show satisfactory uptake of interventions with significant improvements in quality of care and lives saved. Better equipped and trained birth attendants are more confident and skilled in providing care. Additionally, local data-driven feedback has shown to drive continuous quality of care improvement initiatives, which is essential to increase perinatal and maternal survival. Strengths of this research project include integration of innovative tools with existing national guidelines, local data-driven decision-making and training. Limitations include the stepwise cluster implementation design that may lead to contamination of the intervention, and/or inability to address the shortage of healthcare workers and medical supplies beyond the project scope. TRIAL REGISTRATION: Name of Trial Registry: ISRCTN Registry. TRIAL REGISTRATION NUMBER: ISRCTN30541755 . Date of Registration: 12/10/2020. Type of registration: Prospectively Registered.

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BACKGROUND: Post-exposure prophylaxis (PEP) is a well-established strategy for the prevention of infectious diseases, in which recently exposed people take a short course of medication to prevent infection. The primary objective of the COVID-19 Ring-based Prevention Trial with lopinavir/ritonavir (CORIPREV-LR) is to evaluate the efficacy of a 14-day course of oral lopinavir/ritonavir as PEP against COVID-19 among individuals with a high-risk exposure to a confirmed case. METHODS: This is an open-label, multicenter, 1:1 cluster-randomized trial of LPV/r 800/200 mg twice daily for 14 days (intervention arm) versus no intervention (control arm), using an adaptive approach to sample size calculation. Participants will be individuals aged > 6 months with a high-risk exposure to a confirmed COVID-19 case within the past 7 days. A combination of remote and in-person study visits at days 1, 7, 14, 35, and 90 includes comprehensive epidemiological, clinical, microbiologic, and serologic sampling. The primary outcome is microbiologically confirmed COVID-19 infection within 14 days after exposure, defined as a positive respiratory tract specimen for SARS-CoV-2 by polymerase chain reaction. Secondary outcomes include safety, symptomatic COVID-19, seropositivity, hospitalization, respiratory failure requiring ventilator support, mortality, psychological impact, and health-related quality of life. Additional analyses will examine the impact of LPV/r on these outcomes in the subset of participants who test positive for SARS-CoV-2 at baseline. To detect a relative risk reduction of 40% with 80% power at α = 0.05, assuming the secondary attack rate in ring members (p0) = 15%, 5 contacts per case and intra-class correlation coefficient (ICC) = 0.05, we require 110 clusters per arm, or 220 clusters overall and approximately 1220 enrollees after accounting for 10% loss-to-follow-up. We will modify the sample size target after 60 clusters, based on preliminary estimates of p0, ICC, and cluster size and consider switching to an alternative drug after interim analyses and as new data emerges. The primary analysis will be a generalized linear mixed model with logit link to estimate the effect of LPV/r on the probability of infection. Participants who test positive at baseline will be excluded from the primary analysis but will be maintained for additional analyses to examine the impact of LPV/r on early treatment. DISCUSSION: Harnessing safe, existing drugs such as LPV/r as PEP could provide an important tool for control of the COVID-19 pandemic. Novel aspects of our design include the ring-based prevention approach, and the incorporation of remote strategies for conducting study visits and biospecimen collection. TRIAL REGISTRATION: This trial was registered at www.ClinicalTrials.gov ( NCT04321174 ) on March 25, 2020.

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The reduced efficiency of the cluster randomized trial design may be compensated by implementing a multi-period design. The trial then becomes longitudinal, with a risk of intermittently missing observations and dropout. This paper studies the effect of missing data on design efficiency in trials where the periods are the days of the week and clusters are followed for at least one week. The multilevel model with a decaying correlation structure is used to relate outcome to period and treatment condition. The variance of the treatment effect estimator is used to measure efficiency. When there is no data loss, efficiency increases with increasing number of subjects per day and number of weeks. Different weekly measurement schemes are used to evaluate the impact of planned missing data designs: the loss of efficiency due to measuring on fewer days is largest for few subjects per day and few weeks. Dropout is modeled by the Weibull survival function. The loss of efficiency due to dropout increases when more clusters drop out during the course of the trial, especially if the risk of dropout is largest at the beginning of the trial. The largest loss is observed for few subjects per day and a large number of weeks. An example of the effect of waiting room environments in reducing stress in dental care shows how different design options can be compared. An R Shiny app allows researchers to interactively explore various design options and to choose the best design for their trial.

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OBJECTIVES: To determine the effect of vitamin D supplementation on disease progression and post-exposure prophylaxis for COVID-19 infection. We hypothesize that high-dose vitamin D3 supplementation will reduce risk of hospitalization/death among those with recently diagnosed COVID-19 infection and will reduce risk of COVID-19 infection among their close household contacts. METHODS: We report the rationale and design of a planned pragmatic, cluster randomized, double-blinded trial (N = 2700 in total nationwide), with 1500 newly diagnosed individuals with COVID-19 infection, together with up to one close household contact each (~1200 contacts), randomized to either vitamin D3 (loading dose, then 3200 IU/day) or placebo in a 1:1 ratio and a household cluster design. The study duration is 4 weeks. The primary outcome for newly diagnosed individuals is the occurrence of hospitalization and/or mortality. Key secondary outcomes include symptom severity scores among cases and changes in the infection (seroconversion) status for their close household contacts. Changes in vitamin D 25(OH)D levels will be assessed and their relation to study outcomes will be explored. CONCLUSIONS: The proposed pragmatic trial will allow parallel testing of vitamin D3 supplementation for early treatment and post-exposure prophylaxis of COVID-19. The household cluster design provides a cost-efficient approach to testing an intervention for reducing rates of hospitalization and/or mortality in newly diagnosed cases and preventing infection among their close household contacts.

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For most chronic medical conditions, multiple medications are available and prescribers often have limited evidence about which therapy is likely to be the most effective and safe for an individual patient. As many patients are exposed every day to medicines that may be less effective than available alternatives, this is of public health importance. Cluster randomised trials of prescribing policy offer an opportunity to rapidly obtain evidence of comparative effectiveness and safety. These trials can pose a low risk to patients and cause minimal disruption to usual care. Despite the potential scientific value of this approach, there remain valid concerns about consent, medication switching and the use of routinely collected data in research. We discuss these concerns with reference to an ongoing pilot study (Evaluating Diuretics in Normal Care (EVIDENCE) - a cluster randomised evaluation of hypertension prescribing policy, ISRCTN 46635087, registered 11 August 2017).

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BACKGROUND: The rising number of nursing home (NH) residents and their increasingly complex treatment needs pose a challenge to the German health care system. In Germany, there is no specialized geriatric medical care for NH residents. Nursing staff and general practitioners (GPs) in particular have to compensate for the additional demand, which is compounded by organizational and structural hurdles. As a result, avoidable emergency calls and hospital admissions occur. In the SaarPHIR project (Saarländische PflegeHeimversorgung Integriert Regelhaft), a complex intervention focusing on a medical care concept was developed in a participatory practice-based approach involving NH representatives and GPs. The complex intervention addresses the collaboration between nurses and GPs and aims to help restructure and optimize the existing daily care routine. It is expected to improve the medical care of geriatric patients in NHs and reduce stressful, costly hospital admissions. The intervention was pilot-tested during the first 12 months of the project. In the present study, its effectiveness, cost-effectiveness, and safety will be evaluated. METHODS: The study is a cluster-randomized controlled trial, comparing an intervention group with a control group. The intervention includes a concept of interprofessional collaboration, in which GPs group into regional cooperating teams. Teams are encouraged to cooperate more closely with NH staff and to provide on-call schedules, pre-weekend visits, joint team meetings, joint documentation, and improved medication safety. At least 32 NHs in Saarland, Germany (with at least 50 residents each) will be included and monitored for 12 months. The primary endpoint is hospitalization. Secondary endpoints are quality of life, quality of care, and medication safety. The control group receives treatment as usual. Process evaluation and health economic evaluation accompany the study. The data set contains claims data from German statutory health insurance companies as well as primary data. Analysis will be conducted using a generalized linear mixed model. CONCLUSION: A reduction in hospital admissions of NH residents and relevant changes in secondary endpoints are expected. In turn, these will have a positive impact on the economic assessment. TRIAL REGISTRATION: German Clinical Trials Register: DRKS00017129. Registered on 23 April 2019. https://www.drks.de/drks_web/setLocale_EN.do.

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BACKGROUND: More than half of women in India are anemic. Anemia can result in fatigue, poor work productivity, higher risk of pre-term delivery, and maternal mortality. The Indian government has promoted the use of iron-folic acid supplements (IFA) for the prevention and treatment of anemia for the past five decades, but uptake remains low and anemia prevalence high. Current programs target individual-level barriers among pregnant women and adolescents, but a more comprehensive approach that targets multiple levels among all women of reproductive age is needed to increase uptake of IFA and iron-rich foods. METHODS: The Reduction in Anemia through Normative Innovations (RANI) project is a norms-based intervention to reduce anemia among women of reproductive age. We will evaluate the intervention through a clustered randomized controlled trial in Odisha, India. We will collect data at three time points (baseline, midline, and end line). For the study, we selected 89 clusters of villages, which we randomized into treatment and control on a 1:1 basis. The treatment arm will receive the RANI project components while the control arm will receive usual care. Fifteen clusters (40-41 villages) were selected and 4000 women (2000 in each arm) living in the selected clusters will be randomly selected to take part in data collection. Women in both study arms will have their hemoglobin concentrations measured. They will also complete in-person surveys about their knowledge, attitudes, perceptions of iron folic acid supplements, and nutritional intake. We will also select a smaller cohort of 300 non-pregnant women (150 in each arm) from this cohort for additional physical activity and cognitive testing. We will conduct both within- and between-group comparisons (treatment and control) at baseline, midline and end line using t-tests. We will also conduct structural equation modeling to examine how much each factor accounts for IFA use and hemoglobin levels. DISCUSSION: This RCT will enable us to examine whether a social norms-based intervention can increase uptake of iron folic acid supplements and iron rich foods to reduce anemia. TRIAL REGISTRATION: This trial was registered with Clinical Trial Registry- India (CTRI) (CTRI/2018/10/016186) on 29 October 2018.

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In cluster randomized trials (CRTs), the outcome of interest is often a count at the cluster level. This occurs, for example, in evaluating an intervention with the outcome being the number of infections of a disease such as HIV or dengue or the number of hospitalizations in the cluster. Standard practice analyzes these counts through cluster outcome rates using an appropriate denominator (eg, population size). However, such denominators are sometimes unknown, particularly when the counts depend on a passive community surveillance system. We consider direct comparison of the counts without knowledge of denominators, relying on randomization to balance denominators. We also focus on permutation tests to allow for small numbers of randomized clusters. However, such approaches are subject to bias when there is differential ascertainment of counts across arms, a situation that may occur in CRTs that cannot implement blinded interventions. We suggest the use of negative control counts as a method to remove, or reduce, this bias, discussing the key properties necessary for an effective negative control. A current example of such a design is the recent extension of test-negative designs to CRTs testing community-level interventions. Via simulation, we compare the performance of new and standard estimators based on CRTs with negative controls to approaches that only use the original counts. When there is no differential ascertainment by intervention arm, the count-only approaches perform comparably to those using debiasing negative controls. However, under even modest differential ascertainment, the count-only estimators are no longer reliable.

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BACKGROUND: Evidence from prospectively designed studies to guide on-site monitoring practices for randomized trials is limited. A cluster randomized study, nested within the Strategic Timing of AntiRetroviral Treatment (START) trial, was conducted to evaluate on-site monitoring. METHODS: Sites were randomized to either annual on-site monitoring or no on-site monitoring. All sites were centrally monitored, and local monitoring was carried out twice each year. Randomization was stratified by country and projected enrollment in START. The primary outcome was a participant-level composite outcome including components for eligibility errors, consent violations, use of antiretroviral treatment not recommended by protocol, late reporting of START primary and secondary clinical endpoints (defined as the event being reported more than 6 months from occurrence), and data alteration and fraud. Logistic regression fixed effect hierarchical models were used to compare on-site versus no on-site monitoring for the primary composite outcome and its components. Odds ratios and 95% confidence intervals comparing on-site monitoring versus no on-site monitoring are cited. RESULTS: In total, 99 sites (2107 participants) were randomized to receive annual on-site monitoring and 97 sites (2264 participants) were randomized to be monitored only centrally and locally. The two monitoring groups were well balanced at entry. In the on-site monitoring group, 469 annual on-site monitoring visits were conducted, and 134 participants (6.4%) in 56 of 99 sites (57%) had a primary monitoring outcome. In the no on-site monitoring group, 85 participants (3.8%) in 34 of 97 sites (35%) had a primary monitoring outcome (odds ratio = 1.7; 95% confidence interval: 1.1-2.7; p = 0.03). Informed consent violations accounted for most outcomes in each group (56 vs 41 participants). The largest odds ratio was for eligibility violations (odds ratio = 12.2; 95% confidence interval: 1.8-85.2; p = 0.01). The number of participants with a late START primary endpoint was similar for each monitoring group (23 vs 16 participants). Late START grade 4 and unscheduled hospitalization events were found for 34 participants in the on-site monitoring group and 19 participants in the no on-site monitoring group (odds ratio = 2.0; 95% confidence interval: 1.1-3.7; p = 0.02). There were no cases of data alteration or fraud. Based on the travel budget for on-site monitoring and the hours spent conducting on-site monitoring, the estimated cost of on-site monitoring was over US$2 million. CONCLUSION: On-site monitoring led to the identification of more eligibility and consent violations and START clinical events being reported more than 6 months from occurrence as compared to no on-site monitoring. Considering the nature of the excess monitoring outcomes identified at sites receiving on-site monitoring, as well as the cost of on-site monitoring, the value to the START study was limited.

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In stepped wedge designs (SWD), clusters are randomized to the time period during which new patients will receive the intervention under study in a sequential rollout over time. By the study's end, patients at all clusters receive the intervention, eliminating ethical concerns related to withholding potentially efficacious treatments. This is a practical option in many large-scale public health implementation settings. Little statistical theory for these designs exists for binary outcomes. To address this, we utilized a maximum likelihood approach and developed numerical methods to determine the asymptotic power of the SWD for binary outcomes. We studied how the power of a SWD for detecting risk differences varies as a function of the number of clusters, cluster size, the baseline risk, the intervention effect, the intra-cluster correlation coefficient, and the time effect. We studied the robustness of power to the assumed form of the distribution of the cluster random effects, as well as how power is affected by variable cluster size. % SWD power is sensitive to neither, in contrast to the parallel cluster randomized design which is highly sensitive to variable cluster size. We also found that the approximate weighted least square approach of Hussey and Hughes (2007, Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials 28, 182-191) for binary outcomes under-estimates the power in some regions of the parameter spaces, and over-estimates it in others. The new method was applied to the design of a large-scale intervention program on post-partum intra-uterine device insertion services for preventing unintended pregnancy in the first 1.5 years following childbirth in Tanzania, where it was found that the previously available method under-estimated the power.

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BACKGROUND: In cluster-randomized controlled trials (C-RCTs), covariate-constrained randomization (CCR) methods efficiently control imbalance in multiple baseline cluster-level variables, but the choice of imbalance metric to define the subset of "adequately balanced" possible allocation schemes for C-RCTs involving more than two arms and continuous variables is unclear. In an ongoing three-armed C-RCT, we chose the min(three Kruskal-Wallis [KW] test P values) > 0.30 as our metric. We use simulation studies to explore the performance of this and other metrics of baseline variable imbalance in CCR. METHODS: We simulated three continuous variables across three arms under varying allocation ratios and assumptions. We compared the performance of min(analysis of variance [ANOVA] P value) > 0.30, min(KW P value) > 0.30, multivariate analysis of variance (MANOVA) P value > 0.30, min(nine possible t test P values) > 0.30, and min(Wilcoxon rank-sum [WRS] P values) > 0.30. RESULTS: Pairwise comparison metrics (t test and WRS) tended to be the most conservative, providing the smallest subset of allocation schemes (10%-13%) meeting criteria for acceptable balance. Sensitivity of the min(t test P values) > 0.30 for detecting non-trivial imbalance was 100% for both hypothetical and resampled simulation scenarios. The KW criterion maintained higher sensitivity than both the MANOVA and ANOVA criteria (89% to over 99%) but was not as sensitive as pairwise criteria. CONCLUSIONS: Our criterion, the KW P value > 0.30, to signify "acceptable" balance was not the most conservative, but it appropriately identified imbalance in the majority of simulations. Since all are related, CCR algorithms involving any of these imbalance metrics for continuous baseline variables will ensure robust simultaneous control over multiple continuous baseline variables, but we recommend care in determining the threshold of "acceptable" levels of (im)balance. TRIAL REGISTRATION: This trial is registered on ClinicalTrials.gov (initial post: December 1, 2016; identifier: NCT02979444 ).

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BACKGROUND: In cluster randomized controlled trials (RCTs) for emergency medical services (EMS) system, we encounter the situation that the actual cluster size and ratio of allocated patients between two groups eventually differ from those used for sample size estimation because of the nature of patient enrollment. In such trials, estimations of effect size of test intervention and intra-cluster correlation coefficient (ICC) used for sample size estimation are also difficult. To improve efficient management on clinical cluster RCTs, we need to understand the effect of such inconsistencies of the design parameters on the type I error rate and statistical power of testing. METHODS: We planned the trial which evaluated the 1-month favorable neurological survival of out-of-hospital cardiac arrest patients with or without real-time feedback, debriefing, and retraining system by EMS personnel. Under the conditions that we possibly encountered in this trial, we examined the effect of inconsistencies in the actual ICC, cluster size, and ratio of patient allocation with those expected for sample size estimation on the type I error rate and power, using simulation studies. We further investigated the contribution of incorporating sample size re-estimation, based on the results of interim analysis of the trial, on the power increase. RESULTS: This simulation study showed that the inconsistencies of cluster size and patient allocation ratio decreased the power by 5-10% in some cases. In addition, the power decreased by 3-4% when the actual ICC was larger than that expected for sample size estimation. Furthermore, the use of a generalized estimating equation method to evaluate the difference in the 1-month favorable neurological survival between two groups caused inflation of type I error rate. Finally, the increase in power by incorporating sample size re-estimation was limited. CONCLUSIONS: We identified remarkable effects of sample size estimation and re-estimations in a cluster RCT for real-time feedback, debriefing, and retraining system of cardiopulmonary resuscitation for out-of-hospital cardiac arrests. The estimation of design parameters for sample size estimation is generally challenging in cluster RCTs for EMS system; therefore, it is important to conduct a trial simulation that assesses the statistical performances under sample sizes based on the various expected values of the design parameters before beginning the trial.