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Background A cluster randomised trial is a randomised controlled trial in which groups of individuals (clusters) are randomised to treatment arms. Stepped wedge cluster randomised trials are a type of cluster randomised trial where clusters are randomised to sequences. These trial designs are important for impacting decision-making, and it is therefore important that they be well-conducted and reported. Main body In November 2018, we created a new website dedicated to cluster randomised trials, including stepped wedge designs: https://clusterrandomisedtrials.qmul.ac.uk/ . The idea for the website emerged from the conference on Current Developments in Cluster Randomised Trials and Stepped Wedge Designs held in November 2016 at Queen Mary University of London, with the aim to provide an online resource to facilitate quality trials and methodological research on these types of trial. The website is divided into sections covering Design, Analysis and Reporting for traditional (i.e. parallel two-arm) cluster randomised trials and stepped wedge designs and contains resources in the form of hyperlinks to relevant papers along with brief explanations. A noticeboard page provides details on announcements, events, and past events. Conclusion We aim to keep the site updated with the latest publications and events related to cluster randomised trials, and welcome suggestions from the research community on further resources or events to add. We hope that the site will facilitate high-quality traditional and stepped wedge cluster randomised trials.

Background Large-scale quality improvement interventions demand robust trial designs with flexibility for delivery in different contexts, particularly during a pandemic. We describe innovative features of a batched stepped wedge trial, ESCP sAfe Anastomosis proGramme in CoLorectal SurgEry (EAGLE), intended to reduce anastomotic leak following right colectomy, and reflect on lessons learned about the implementation of quality improvement programmes on an international scale. Methods Surgical units were recruited and randomised in batches to receive a hospital-level education intervention designed to reduce anastomotic leak, either before, during, or following data collection. All consecutive patients undergoing right colectomy were included. Online learning, patient risk stratification and an in-theatre checklist constituted the intervention. The study was powered to detect an absolute risk reduction of anastomotic leak from 8.1 to 5.6%. Statistical efficiency was optimised using an incomplete stepped wedge trial design and study batches analysed separately then meta-analysed to calculate the intervention effect. An established collaborative group helped nurture strong working relationships between units/countries and a prospectively designed process evaluation will enable evaluation of both the intervention and its implementation. Results The batched trial design allowed sequential entry of clusters, targeted research training and proved to be robust to pandemic interruptions. Staggered start times in the incomplete stepped wedge design with long lead-in times can reduce motivation and engagement and require careful administration. Conclusion EAGLE’s robust but flexible study design allowed completion of the study across globally distributed geographical locations in spite of the pandemic. The primary outcome analysed in conjunction with the process evaluation will ensure a rich understanding of the intervention and the effects of the study design. Trial registration National Institute of Health Research Clinical Research Network portfolio IRAS ID: 272,250. Health Research Authority approval 18 October 2019. ClinicalTrials.gov, identifier NCT04270721, protocol ID RG_19196.

Background Cluster randomised trials often randomise a small number of units, putting them at risk of poor balance of covariates across treatment arms. Covariate constrained randomisation aims to reduce this risk by removing the worst balanced allocations from consideration. This is known to provide only a small gain in power over that averaged under simple randomisation and is likely influenced by the number and prognostic effect of the covariates. We investigated the performance of covariate constrained randomisation in comparison to the worst balanced allocations, and considered the impact on the power of the prognostic effect and number of covariates adjusted for in the analysis. Methods Using simulation, we examined the Monte Carlo type I error rate and power of cross-sectional, two-arm parallel cluster-randomised trials with a continuous outcome and four binary cluster-level covariates, using either simple or covariate constrained randomisation. Data were analysed using a small sample corrected linear mixed-effects model, adjusted for some or all of the binary covariates. We varied the number of clusters, intra-cluster correlation, number and prognostic effect of covariates balanced in the randomisation and adjusted in the analysis, and the size of the candidate set from which the allocation was selected. For each scenario, 20,000 simulations were conducted. Results When compared to the worst balanced allocations, covariate constrained randomisation with an adjusted analysis provided gains in power of up to 20 percentage points. Even with analysis-based adjustment for those covariates balanced in the randomisation, the type I error rate was not maintained when the intracluster correlation is very small (0.001). Generally, greater power was achieved when more prognostic covariates are restricted in the randomisation and as the size of the candidate set decreases. However, adjustment for weakly prognostic covariates lead to a loss in power of up to 20 percentage points. Conclusions When compared to the worst balanced allocations, covariate constrained randomisation provides moderate to substantial improvements in power. However, the prognostic effect of the covariates should be carefully considered when selecting them for inclusion in the randomisation.

For group‐randomized trials, randomization inference based on rank statistics provides robust, exact inference against nonnormal distributions. However, in a matched‐pair design, the currently available rank‐based statistics lose significant power compared to normal linear mixed model (LMM) test statistics when the LMM is true. In this article, we investigate and develop an optimal test statistic over all statistics in the form of the weighted sum of signed Mann‐Whitney‐Wilcoxon statistics under certain assumptions. This test is almost as powerful as the LMM even when the LMM is true, but it is much more powerful for heavy tailed distributions. A simulation study is conducted to examine the power.

Schedule control and supervisor support for family and personal life may help employees manage the work-family interface. Existing data and research designs, however, have made it difficult to conclusively identify the effects of these work resources. This analysis utilizes a group-randomized trial in which some units in an information technology workplace were randomly assigned to participate in an initiative, called STAR, that targeted work practices, interactions, and expectations by (1) training supervisors on the value of demonstrating support for employees' personal lives and (2) prompting employees to reconsider when and where they work. We find statistically significant, although modest, improvements in employees' work-family conflict and family time adequacy, and larger changes in schedule control and supervisor support for family and personal life. We find no evidence that this intervention increased work hours or perceived job demands, as might have happened with increased permeability of work across time and space. Subgroup analyses suggest the intervention brought greater benefits to employees more vulnerable to work-family conflict. This study uses a rigorous design to investigate deliberate organizational changes and their effects on work resources and the work-family interface, advancing our understanding of the impact of social structures on individual lives.

The orthographic forms (spellings) of second language (L2) words and sounds affect the pronunciation and awareness of L2 sounds, even after lengthy naturalistic exposure. This study investigated whether instruction could reduce the effects of English orthographic forms on Italian native speakers’ pronunciation and awareness of L2 English sounds. Italians perceive, produce, and judge the same sound as a short sound if it is spelled with one letter and as a long sound if it is spelled with a digraph, due to L1 Italian grapheme-phoneme correspondence (GPC) rules whereby double consonant letters represent long consonants. Totally, 100 Italian learners of English were allocated to two conditions (final n = 88). The participants in the explicit GPC (EGPC) condition discovered English GPC rules relating to sound length through reflection, explicit teaching, and practice; the participants in the passive exposure condition practiced the same words as the EGPC participants, but with no mention of GPCs. Pre- and postintervention production (delayed word repetition) and phonological awareness (rhyme judgment) tasks revealed no positive effects of the instruction. GPC instruction appears to be ineffective in reducing orthographic effects on L2 phonology. Orthographic effects may be impervious to change, whether by naturalistic exposure or by instruction.

In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator.

Background Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model’s appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random. Methods We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics. Results When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081. Conclusions Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally. Trial registration ClinicalTrials.gov, ID: NCT02804698 .

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines.

Background Multiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model. Methods Continuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group. Results Results indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance. Conclusion The time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.