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Background Asymptomatic and paucisymptomatic infections account for a substantial portion of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions. The value of intensified screening strategies, especially in emergency departments (EDs), in reaching asymptomatic and paucisymptomatic patients and helping to improve detection and reduce transmission has not been documented. The objective of this study was to evaluate in EDs whether an intensified SARS-CoV-2 screening strategy combining nurse-driven screening for asymptomatic/paucisymptomatic patients with routine practice (intervention) could contribute to higher detection of SARS-CoV-2 infections compared to routine practice alone, including screening for symptomatic or hospitalized patients (control). Methods and findings We conducted a cluster-randomized, two-period, crossover trial from February 2021 to May 2021 in 18 EDs in the Paris metropolitan area, France. All adults visiting the EDs were eligible. At the start of the first period, 18 EDs were randomized to the intervention or control strategy by balanced block randomization with stratification, with the alternative condition being applied in the second period. During the control period, routine screening for SARS-CoV-2 included screening for symptomatic or hospitalized patients. During the intervention period, in addition to routine screening practice, a questionnaire about risk exposure and symptoms and a SARS-CoV-2 screening test were offered by nurses to all remaining asymptomatic/paucisymptomatic patients. The primary outcome was the proportion of newly diagnosed SARS-CoV-2-positive patients among all adults visiting the 18 EDs. Primary analysis was by intention-to-treat. The primary outcome was analyzed using a generalized linear mixed model (Poisson distribution) with the center and center by period as random effects and the strategy (intervention versus control) and period (modeled as a weekly categorical variable) as fixed effects with additional adjustment for community incidence. During the intervention and control periods, 69,248 patients and 69,104 patients, respectively, were included for a total of 138,352 patients. Patients had a median age of 45.0 years [31.0, 63.0], and women represented 45.7% of the patients. During the intervention period, 6,332 asymptomatic/paucisymptomatic patients completed the questionnaire; 4,283 were screened for SARS-CoV-2 by nurses, leading to 224 new SARS-CoV-2 diagnoses. A total of 1,859 patients versus 2,084 patients were newly diagnosed during the intervention and control periods, respectively (adjusted analysis: 26.7/1,000 versus 26.2/1,000, adjusted relative risk: 1.02 (95% confidence interval (CI) [0.94, 1.11]; p = 0.634)). The main limitation of this study is that it was conducted in a rapidly evolving epidemiological context. Conclusions The results of this study showed that intensified screening for SARS-CoV-2 in EDs was unlikely to identify a higher proportion of newly diagnosed patients. Trial registration Trial registration number: ClinicalTrials.gov NCT04756609., Author(s): Judith Leblanc 1,*, Lisbeth Dusserre-Telmon 2, Anthony Chauvin 3, Tabassome Simon 4, Chiara E. Sabbatini 5, Karla Hemming 6, Vittoria Colizza 5, Laurence Bérard 7, Jérome Convert 8, Sonia [...]

Cluster‐randomized trials (CRTs) involve randomizing entire groups of participants—called clusters—to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual‐level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual‐level covariate adjustment strategies for estimating the participant‐average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite‐sample performance of the bias‐corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant‐average treatment effect. To illustrate the methods for individual‐level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether a Bayesian interpretation might help prevent misinterpretation of statistical findings and support authors to differentiate evidence of no effect from statistical uncertainty. Design: A Bayesian re‐analysis to determine posterior probabilities of clinically important effects (e.g., a large effect is set at a 4 percentage point difference and a trivial effect to be within a 0.5 percentage point difference). Posterior probabilities greater than 95% are considered as strong statistical evidence, and less than 95% as inconclusive. Sample: 150 major women's health trials with binary outcomes. Main Outcome Measures: Posterior probabilities of large, moderate, small and trivial effects. Results: Under frequentist methods, 48 (32%) were statistically significant (p‐value ≤ 0.05) and 102 (68%) statistically non‐significant. The frequentist and Bayesian point estimates and confidence intervals showed strong concordance. Of the statistically non‐significant trials (n = 102), the Bayesian approach classified the majority (94, 92%) as inconclusive, neither able to confirm or refute effectiveness. A small number of statistically non‐significant findings (8, 8%) were classified as having strong statistical evidence of an effect. Conclusions: Whilst almost all trials report confidence intervals, in practice most statistical findings are interpreted on the basis of statistical significance, mostly concluding evidence of no effect. Findings here suggest the majority are likely uncertain. A Bayesian approach could help differentiate evidence of no effect from statistical uncertainty. [ABSTRACT FROM AUTHOR]

There are multiple possible cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at each time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding experimental weights, extensions to non-Gaussian models, and robust optimality. We present results from multiple cluster trial examples that compare the different methods, including determination of the optimal allocation of clusters across a set of cluster sequences and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures. [ABSTRACT FROM AUTHOR]

Many workplaces offer health and wellbeing initiatives to their staff as recommended by international and national health organisations. Despite their potential, the influence of these initiatives on health behaviour appears limited and evaluations of their effectiveness are rare. In this research, we propose evaluating the effectiveness of an established behaviour change intervention in a new workplace context. The intervention, 'mental contrasting plus implementation intentions', supports staff in achieving their health and wellbeing goals by encouraging them to compare the future with the present and to develop a plan for overcoming anticipated obstacles. We conducted a systematic review that identified only three trials of this intervention in workplaces and all of them were conducted within healthcare organisations. Our research will be the first to evaluate the effectiveness of mental contrasting outside a solely healthcare context. We propose including staff from 60 organisations, 30 in the intervention and 30 in a waitlisted control group. The findings will contribute to a better understanding of how to empower and support staff to improve their health and wellbeing. Trial registration:ISRCTN17828539. [ABSTRACT FROM AUTHOR]

In this article, we derive and compare methods to derive P‐values and sets of confidence intervals with strong control of the family‐wise error rates and coverage for estimates of treatment effects in cluster randomized trials with multiple outcomes. There are few methods for P‐value corrections and deriving confidence intervals, limiting their application in this setting. We discuss the methods of Bonferroni, Holm, and Romano and Wolf and adapt them to cluster randomized trial inference using permutation‐based methods with different test statistics. We develop a novel search procedure for confidence set limits using permutation tests to produce a set of confidence intervals under each method of correction. We conduct a simulation‐based study to compare family‐wise error rates, coverage of confidence sets, and the efficiency of each procedure in comparison to no correction using both model‐based standard errors and permutation tests. We show that the Romano‐Wolf type procedure has nominal error rates and coverage under non‐independent correlation structures and is more efficient than the other methods in a simulation‐based study. We also compare results from the analysis of a real‐world trial. [ABSTRACT FROM AUTHOR]