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BACKGROUND: Simulation is an important tool for assessing the performance of statistical methods for the analysis of data and for the planning of studies. While methods are available for the simulation of correlated binary random variables, all have significant practical limitations for simulating outcomes from longitudinal cluster randomised trial designs, such as the cluster randomised crossover and the stepped wedge trial designs. For these trial designs as the number of observations in each cluster increases these methods either become computationally infeasible or their range of allowable correlations rapidly shrinks to zero. METHODS: In this paper we present a simple method for simulating binary random variables with a specified vector of prevalences and correlation matrix. This method allows for the outcome prevalence to change due to treatment or over time, and for a 'nested exchangeable' correlation structure, in which observations in the same cluster are more highly correlated if they are measured in the same time period than in different time periods, and where different individuals are measured in each time period. This means that our method is also applicable to more general hierarchical clustered data contexts, such as students within classrooms within schools. The method is demonstrated by simulating 1000 datasets with parameters matching those derived from data from a cluster randomised crossover trial assessing two variants of stress ulcer prophylaxis. RESULTS: Our method is orders of magnitude faster than the most well known general simulation method while also allowing a much wider range of correlations than alternative methods. An implementation of our method is available in an R package NestBin. CONCLUSIONS: This simulation method is the first to allow for practical and efficient simulation of large datasets of binary outcomes with the commonly used nested exchangeable correlation structure. This will allow for much more effective testing of designs and inference methods for longitudinal cluster randomised trials with binary outcomes.

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BACKGROUND: The massive scale-up of long-lasting insecticidal nets (LLIN) has led to a major reduction in malaria burden in many sub-Saharan African (SSA) countries. The World Health Organization (WHO) has recently issued a strong recommendation for the use of chlorfenapyr-pyrethroid LLINs compared to standard pyrethroid-only LLINs in areas of high insecticide resistance intensity. However, there is still a lack of conclusive evidence on the efficacy of piperonyl butoxide-pyrethroid (PBO-py) LLINs, especially in West Africa, where vector composition and resistance mechanisms may be different from vectors in East Africa. METHODS: This is a three-arm, superiority, triple-blinded, cluster randomised trial, with village as the unit of randomisation. This study conducted in Côte d'Ivoire will evaluate the efficacy on epidemiological and entomological outcomes of (1) the control arm: MAGNet® LN, which contains the pyrethroid, alpha-cypermethrin, (2) VEERALIN® LN, a net combining the synergist PBO and alpha-cypermethrin, and (3) Interceptor® G2 LN, which incorporates chlorfenapyr and alpha-cypermethrin, two adulticides with different mechanisms of action. A total of 33 villages with an average of 200 households per village will be identified, mapped, and randomised in a ratio of 1:1:1. Nets will be distributed at a central point following national guidelines with 1 net for every 2 people. The primary outcome of the trial will be incidence of malaria cases (confirmed by rapid diagnostic test (RDT)) in a cohort of 50 children aged 6 months to 10 years in each cluster, followed for 12 months (active case detection). Secondary outcomes are cross-sectional community prevalence of malaria infection (confirmed by RDT) in the study population at 6 and 12 months post-intervention (50 randomly selected persons per cluster), vector density, entomological inoculation rate (EIR), and phenotypic and genotypic insecticide resistance at baseline and 12 months post-intervention in 3 sentinel villages in each treatment arm. DISCUSSION: In addition to generating further evidence for next-generation LLINs, this study will also provide the first evidence for pyrethroid-PBO nets in a West African setting. This could further inform WHO recommendations on the pragmatic use of pyrethroid-PBO nets. TRIAL REGISTRATION: ClinicalTrials.gov NCT05796193. Registered on April 3, 2023.

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BACKGROUND: Standard stepped wedge trials, where clusters switch from the control to the intervention condition in a staggered manner, can be costly and burdensome. Recent work has shown that the amount of information contributed by each cluster in each period differs, with some cluster-periods contributing a relatively small amount of information. We investigate the patterns of the information content of cluster-period cells upon iterative removal of low-information cells, assuming a model for continuous outcomes with constant cluster-period size, categorical time period effects, and exchangeable and discrete-time decay intracluster correlation structures. METHODS: We sequentially remove pairs of "centrosymmetric" cluster-period cells from an initially complete stepped wedge design which contribute the least amount of information to the estimation of the treatment effect. At each iteration, we update the information content of the remaining cells, determine the pair of cells with the lowest information content, and repeat this process until the treatment effect cannot be estimated. RESULTS: We demonstrate that as more cells are removed, more information is concentrated in the cells near the time of the treatment switch, and in "hot-spots" in the corners of the design. For the exchangeable correlation structure, removing the cells from these hot-spots leads to a marked reduction in study precision and power, however the impact of this is lessened for the discrete-time decay structure. CONCLUSIONS: Removing cluster-period cells distant from the time of the treatment switch may not lead to large reductions in precision or power, implying that certain incomplete designs may be almost as powerful as complete designs.

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BACKGROUND: Cluster-randomised trials often use some form of restricted randomisation, such as stratified- or covariate-constrained randomisation. Minimisation has the potential to balance on more covariates than blocked stratification and can be implemented sequentially unlike covariate-constrained randomisation. Yet, unlike stratification, minimisation has no inbuilt guard to maintain close to a 1:1 allocation. A departure from a 1:1 allocation can be unappealing in a setting with a small number of allocation units such as cluster randomisation which typically include about 30 clusters. METHODS: Using simulation (10,000 per scenario), we evaluate the performance of a range of minimisation procedures on the likelihood of a 1:1 allocation of clusters (10-80 clusters) to treatment arms, along with its performance on covariate imbalance. The range of minimisation procedures includes varying: the proportion of clusters allocated to the least imbalanced arm (known as the stochastic element) - between 0.7 and 1, percentage of first clusters allocated completely at random (known as the bed-in period) - between 0% and 20% and adding 'number of clusters allocated to each arm' as a covariate in the minimisation algorithm. We additionally include a comparison of stratifying and then minimising within key strata (such as country within a multi country cluster trial) as a potential aid to increasing balance. RESULTS: Minimisation is unlikely to result in an exact 1:1 allocation unless the stochastic element is set higher than 0.9. For example, with 20 clusters, 2 binary covariates and setting the stochastic element to 0.7: only 41% of the possible randomisations over the 10,000 simulations achieved a 1:1 allocation. While typical sizes of imbalance were small (a difference of two clusters per arm), allocations as extreme as of 10:10 were observed. Adding the 'number of clusters' into the minimisation algorithm reduces this risk slightly, but covariate imbalance increases slightly. Stratifying and then minimising within key strata improve balance within strata but increase imbalance across all clusters, both on the number of clusters and covariate imbalance. CONCLUSION: In cluster trials, where there are typically about 30 allocation units, when using minimisation, unless the stochastic element is set very high, there is a high risk of not achieving a 1:1 allocation, and a small but nonetheless real risk of an extreme departure from a 1:1 allocation. Stratification with minimisation within key strata (such as country) improves the balance within strata although compromises overall balance.

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BACKGROUND: Delirium is an acute change in behaviour, characterised by a fluctuating course, inattention, and disorganised thinking. For critically ill adults in the intensive care, the incidence of delirium has been reported to be at least 30% and is associated with both short-term and long-term complications, longer hospital stay, increased risk of mortality, and long-term cognitive problems. AIM: The objective of this study was to determine the effectiveness of a nurse-led delirium-prevention protocol in reducing the incidence and duration of delirium among adults admitted to intensive care. METHODS: A hybrid stepped-wedge cluster randomised controlled trial was conducted to assess the effectiveness of the implementation and dissemination of the nurse-led intervention to reduce the incidence and duration of delirium among adults admitted to the four adults intensive care units in the southwest of Sydney, Australia. RESULTS: Between May 2019 and February 2020, over a 10-month period, 2618 admissions, among 2566 patients, were included in the study. After an initial 3-month baseline period, each month there was a random crossover to the nurse-led intervention in one of the four intensive care units, and by the 7th month of the trial, all units were exposed to the intervention for at least 3 months. The incidence of acute delirium was observed to be 10.7% (95% confidence interval [CI] = 9.1-12.4%), compared to 14.1% (95% CI = 12.2-16.2%) during the preintervention (baseline) period (adjusted rate ratio [adjRR] = 0.78, 95% CI = 0.57-1.08, p = 0.134). The average delirium-free-days for these preintervention and postintervention periods were 4.1 days (95% CI = 3.9-4.3) and 4.4 days (95% CI = 4.2-4.5), respectively (adjusted difference = 0.24 days [95% CI = -0.12 to 0.60], p = 0.199). CONCLUSION: Following the introduction of a nurse-led, nonpharmacological intervention to reduce the burden of delirium, among adults admitted to intensive care, we observed no statistically significant decrease in the incidence of delirium or the duration of delirium.

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BACKGROUND: Pragmatic trials aim to generate evidence to directly inform patient, caregiver and health-system manager policies and decisions. Heterogeneity in patient characteristics contributes to heterogeneity in their response to the intervention. However, there are many other sources of heterogeneity in outcomes. Based on the expertise and judgements of the authors, we identify different sources of clinical and methodological heterogeneity, which translate into heterogeneity in patient responses-some we consider as desirable and some as undesirable. For each of them, we discuss and, using real-world trial examples, illustrate how heterogeneity should be managed over the whole course of the trial. MAIN TEXT: Heterogeneity in centres and patients should be welcomed rather than limited. Interventions can be flexible or tailored and control interventions are expected to reflect usual care, avoiding use of a placebo. Co-interventions should be allowed; adherence should not be enforced. All these elements introduce heterogeneity in interventions (experimental or control), which has to be welcomed because it mimics reality. Outcomes should be objective and possibly routinely collected; standardised assessment, blinding and adjudication should be avoided as much as possible because this is not how assessment would be done outside a trial setting. The statistical analysis strategy must be guided by the objective to inform decision-making, thus favouring the intention-to-treat principle. Pragmatic trials should consider including process analyses to inform an understanding of the trial results. Needed data to conduct these analyses should be collected unobtrusively. Finally, ethical principles must be respected, even though this may seem to conflict with goals of pragmatism; consent procedures could be incorporated in the flow of care.

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BACKGROUND: The last 20 years have seen a marked increase in the use of cluster randomised trials (CRTs) in schools to evaluate interventions for improving pupil health outcomes. Schools have limited resources and participating in full-scale trials can be challenging and costly, given their main purpose is education. Feasibility studies can be used to identify challenges with implementing interventions and delivering trials. This systematic review summarises methodological characteristics and objectives of school-based cluster randomised feasibility studies in the United Kingdom (UK). METHODS: We systematically searched MEDLINE from inception to 31 December 2020. Eligible papers were school-based feasibility CRTs that included health outcomes measured on pupils. RESULTS: Of 3285 articles identified, 24 were included. School-based feasibility CRTs have been increasingly used in the UK since the first publication in 2008. Five (21%) studies provided justification for the use of the CRT design. Three (13%) studies provided details of a formal sample size calculation, with only one of these allowing for clustering. The median (IQR; range) recruited sample size was 7.5 (4.5 to 9; 2 to 37) schools and 274 (179 to 557; 29 to 1567) pupils. The most common feasibility objectives were to estimate the potential effectiveness of the intervention (n = 17; 71%), assess acceptability of the intervention (n = 16; 67%), and estimate the recruitment/retention rates (n = 15; 63%). Only one study was used to assess whether cluster randomisation was appropriate, and none of the studies that randomised clusters before recruiting pupils assessed the possibility of recruitment bias. Besides potential effectiveness, cost-effectiveness, and the intra-cluster correlation coefficient, no studies quantified the precision of the feasibility parameter estimates. CONCLUSIONS: Feasibility CRTs are increasingly used in schools prior to definitive trials of interventions for improving health in pupils. The average sample size of studies included in this review would be large enough to estimate pupil-level feasibility parameters (e.g., percentage followed up) with reasonable precision. The review highlights the need for clearer sample size justification and better reporting of the precision with which feasibility parameters are estimated. Better use could be made of feasibility CRTs to assess challenges that are specific to the cluster design. TRIAL REGISTRATION: PROSPERO: CRD42020218993.

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BACKGROUND: When designing and analysing longitudinal cluster randomised trials, such as the stepped wedge, the similarity of outcomes from the same cluster must be accounted for through the choice of a form for the within-cluster correlation structure. Several choices for this structure are commonly considered for application within the linear mixed model paradigm. The first assumes a constant intra-cluster correlation for all pairs of outcomes from the same cluster (the exchangeable/Hussey and Hughes model); the second assumes that correlations of outcomes measured in the same period are higher than outcomes measured in different periods (the block exchangeable model) and the third is the discrete-time decay model, which allows the correlation between pairs of outcomes to decay over time. Currently, there is limited guidance on how to select the most appropriate within-cluster correlation structure. METHODS: We simulated continuous outcomes under each of the three considered within-cluster correlation structures for a range of design and parameter choices, and, using the ASReml-R package, fit each linear mixed model to each simulated dataset. We evaluated the performance of the Akaike and Bayesian information criteria for selecting the correct within-cluster correlation structure for each dataset. RESULTS: For smaller total sample sizes, neither criteria performs particularly well in selecting the correct within-cluster correlation structure, with the simpler exchangeable model being favoured. Furthermore, in general, the Bayesian information criterion favours the exchangeable model. When the cluster auto-correlation (which defines the degree of dependence between observations in adjacent time periods) is large and number of periods is small, neither criteria is able to distinguish between the block exchangeable and discrete time decay models. However, for increasing numbers of clusters, periods, and subjects per cluster period, both the Akaike and Bayesian information criteria perform increasingly well in the detection of the correct within-cluster correlation structure. CONCLUSIONS: With increasing amounts of data, be they number of clusters, periods or subjects per cluster period, both the Akaike and Bayesian information criteria are increasingly likely to select the correct correlation structure. We recommend that if there are sufficient data available when planning a trial, that the Akaike or Bayesian information criterion is used to guide the choice of within-cluster correlation structure in the absence of other compelling justifications for a specific correlation structure. We also suggest that researchers conduct supplementary analyses under alternate correlation structures to gauge sensitivity to the initial choice.

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BACKGROUND: Due to challenges in measuring changes in malaria at low transmission, serology is increasingly being used to complement clinical and parasitological surveillance. Longitudinal studies have shown that serological markers, such as Etramp5.Ag1, can reflect spatio-temporal differences in malaria transmission. However, these markers have yet to be used as endpoints in intervention trials. METHODS: Based on data from a 2017 cluster randomised trial conducted in Zambezi Region, Namibia, evaluating the effectiveness of reactive focal mass drug administration (rfMDA) and reactive vector control (RAVC), this study conducted a secondary analysis comparing antibody responses between intervention arms as trial endpoints. Antibody responses were measured on a multiplex immunoassay, using a panel of eight serological markers of Plasmodium falciparum infection - Etramp5.Ag1, GEXP18, HSP40.Ag1, Rh2.2030, EBA175, PfMSP119, PfAMA1, and PfGLURP.R2. FINDINGS: Reductions in sero-prevalence to antigens Etramp.Ag1, PfMSP119, Rh2.2030, and PfAMA1 were observed in study arms combining rfMDA and RAVC, but only effects for Etramp5.Ag1 were statistically significant. Etramp5.Ag1 sero-prevalence was significantly lower in all intervention arms. Compared to the reference arms, adjusted prevalence ratio (aPR) for Etramp5.Ag1 was 0.78 (95%CI 0.65 - 0.91, p = 0.0007) in the rfMDA arms and 0.79 (95%CI 0.67 - 0.92, p = 0.001) in the RAVC arms. For the combined rfMDA plus RAVC intervention, aPR was 0.59 (95%CI 0.46 - 0.76, p < 0.0001). Significant reductions were also observed based on continuous antibody responses. Sero-prevalence as an endpoint was found to achieve higher study power (99.9% power to detect a 50% reduction in prevalence) compared to quantitative polymerase chain reaction (qPCR) prevalence (72.9% power to detect a 50% reduction in prevalence). INTERPRETATION: While the observed relative reduction in qPCR prevalence in the study was greater than serology, the use of serological endpoints to evaluate trial outcomes measured effect size with improved precision and study power. Serology has clear application in cluster randomised trials, particularly in settings where measuring clinical incidence or infection is less reliable due to seasonal fluctuations, limitations in health care seeking, or incomplete testing and reporting. FUNDING: This study was supported by Novartis Foundation (A122666), the Bill & Melinda Gates Foundation (OPP1160129), and the Horchow Family Fund (5,300,375,400).

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BACKGROUND: The healthy context paradox, originally described with respect to school-level bullying interventions, refers to the generation of differences in mental wellbeing amongst those who continue to experience bullying even after interventions successfully reduce victimisation. Using data from the INCLUSIVE trial of restorative practice in schools, we relate this paradox to the need to theorise potential harms when developing interventions; formulate the healthy context paradox in a more general form defined by mediational relationships and cluster-level interventions; and propose two statistical models for testing the healthy context paradox informed by multilevel mediation methods, with relevance to structural and individual explanations for this paradox. METHODS: We estimated two multilevel mediation models with bullying victimisation as the mediator and mental wellbeing as the outcome: one with a school-level interaction between intervention assignment and the mediator; and one with a random slope component for the student-level mediator-outcome relationship predicted by school-level assignment. We relate each of these models to contextual or individual-level explanations for the healthy context paradox. RESULTS: Neither model suggested that the INCLUSIVE trial represented an example of the healthy context paradox. However, each model has different interpretations which relate to a multilevel understanding of the healthy context paradox. CONCLUSIONS: Greater exploration of intervention harms, especially when those accrue to population subgroups, is an essential step in better understanding how interventions work and for whom. Our proposed tests for the presence of a healthy context paradox provide the analytic tools to better understand how to support development and implementation of interventions that work for all groups in a population. TRIAL REGISTRATION: Current Controlled Trials, ISRCTN10751359 .

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BACKGROUND: Cluster randomised trials (CRTs) are increasingly used to evaluate non-pharmacological interventions for improving child health. Although methodological challenges of CRTs are well documented, the characteristics of school-based CRTs with pupil health outcomes have not been systematically described. Our objective was to describe methodological characteristics of these studies in the United Kingdom (UK). METHODS: MEDLINE was systematically searched from inception to 30th June 2020. Included studies used the CRT design in schools and measured primary outcomes on pupils. Study characteristics were described using descriptive statistics. RESULTS: Of 3138 articles identified, 64 were included. CRTs with pupil health outcomes have been increasingly used in the UK school setting since the earliest included paper was published in 1993; 37 (58%) studies were published after 2010. Of the 44 studies that reported information, 93% included state-funded schools. Thirty six (56%) were exclusively in primary schools and 24 (38%) exclusively in secondary schools. Schools were randomised in 56 studies, classrooms in 6 studies, and year groups in 2 studies. Eighty percent of studies used restricted randomisation to balance cluster-level characteristics between trial arms, but few provided justification for their choice of balancing factors. Interventions covered 11 different health areas; 53 (83%) included components that were necessarily administered to entire clusters. The median (interquartile range) number of clusters and pupils recruited was 31.5 (21 to 50) and 1308 (604 to 3201), respectively. In half the studies, at least one cluster dropped out. Only 26 (41%) studies reported the intra-cluster correlation coefficient (ICC) of the primary outcome from the analysis; this was often markedly different to the assumed ICC in the sample size calculation. The median (range) ICC for school clusters was 0.028 (0.0005 to 0.21). CONCLUSIONS: The increasing pool of school-based CRTs examining pupil health outcomes provides methodological knowledge and highlights design challenges. Data from these studies should be used to identify the best school-level characteristics for balancing the randomisation. Better information on the ICC of pupil health outcomes is required to aid the planning of future CRTs. Improved reporting of the recruitment process will help to identify barriers to obtaining representative samples of schools.

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BACKGROUND: There is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study. CASE STUDY: The E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle-Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation. CONCLUSIONS: The interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.

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Generalised estimating equations with the sandwich standard-error estimator provide a promising method of analysis for stepped wedge cluster randomised trials. However, they have inflated type-one error when used with a small number of clusters, which is common for stepped wedge cluster randomised trials. We present a large simulation study of binary outcomes comparing bias-corrected standard errors from Fay and Graubard; Mancl and DeRouen; Kauermann and Carroll; Morel, Bokossa, and Neerchal; and Mackinnon and White with an independent and exchangeable working correlation matrix. We constructed 95% confidence intervals using a t-distribution with degrees of freedom including clusters minus parameters (DFC-P), cluster periods minus parameters, and estimators from Fay and Graubard (DFFG), and Pan and Wall. Fay and Graubard and an approximation to Kauermann and Carroll (with simpler matrix inversion) were unbiased in a wide range of scenarios with an independent working correlation matrix and more than 12 clusters. They gave confidence intervals with close to 95% coverage with DFFG with 12 or more clusters, and DFC-P with 18 or more clusters. Both standard errors were conservative with fewer clusters. With an exchangeable working correlation matrix, approximated Kauermann and Carroll and Fay and Graubard had a small degree of under-coverage.

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BACKGROUND: We consider the design of stepped wedge trials with continuous recruitment and continuous outcome measures. Suppose we recruit from a fixed number of clusters where eligible participants present continuously, and suppose we have fine control over when each cluster crosses to the intervention. Suppose also that we want to minimise the number of participants, leading us to consider "incomplete" designs (i.e. without full recruitment). How can we schedule recruitment and cross-over at different clusters to recruit efficiently while achieving good precision? METHODS: The large number of possible designs can make exhaustive searches impractical. Instead we consider an algorithm using iterative improvements to hunt for an efficient design. At each iteration (starting from a complete design) a single participant - the one with the smallest impact on precision - is removed, and small changes preserving total sample size are made until no further improvement in precision can be found. RESULTS: Striking patterns emerge. Solutions typically focus recruitment and cross-over on the leading diagonal of the cluster-by-time diagram, but in some scenarios clusters form distinct phases resembling before-and-after designs. CONCLUSIONS: There is much to be learned about optimal design for incomplete stepped wedge trials. Algorithmic searches could offer a practical approach to trial design in complex settings generally.

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Non-adherence to assigned treatment is a common issue in cluster randomised trials. In these settings, the efficacy estimand may also be of interest. Many methodological contributions in recent years have advocated using instrumental variables to identify and estimate the local average treatment effect. However, the clustered nature of randomisation in cluster randomised trials adds to the complexity of such analyses. In this paper, we show that the local average treatment effect can be estimated via two-stage least squares regression using cluster-level summaries of the outcome and treatment received under certain assumptions. We propose the use of baseline variables to adjust the cluster-level summaries before performing two-stage least squares in order to improve efficiency. Implementation needs to account for the reduced sample size, as well as the possible heteroscedasticity, to obtain valid inferences. Simulations are used to assess the performance of two-stage least squares of cluster-level summaries under cluster-level or individual-level non-adherence, with and without weighting and robust standard errors. The impact of adjusting for baseline covariates and of appropriate degrees of freedom correction for inference is also explored. The methods are then illustrated by re-analysing a cluster randomised trial carried out in a specific UK primary care setting. Two-stage least squares estimation using cluster-level summaries provides estimates with small to negligible bias and coverage close to nominal level, provided the appropriate small sample degrees of freedom correction and robust standard errors are used for inference.

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Alternative clinical trial designs and methods are increasingly being used in place of the conventional individually randomised controlled trial (RCT) in high-income and in low-income and middle-income country (LMIC) research. These approaches - including adaptive, cluster-randomised and stepped-wedge designs and controlled human infection models - offer a number of potential advantages, including being more efficient and making the clinical trial process more socially acceptable. However, these designs and methods are generally not familiar to researchers, research ethics committees and regulators and their ethical implications have not received sufficient international attention from the bioethics, research, and policymaking communities working together. The ethics of alternative clinical trial designs and methods in LMIC research was chosen as a topic for the 2017 Global Forum on Bioethics in Research (GFBR). The meeting opened a global dialogue about this emerging issue in research ethics and gave voice to the LMIC perspective. It identified the need to take a multidisciplinary approach and to develop capacity amongst researchers and research ethics committees and regulators to propose, review and regulate these novel designs and methods. Building skills and infrastructure will empower researchers to choose from a broad range of designs and methods and adopt the most scientifically suitable, efficient, ethical and context-appropriate of these. The need for capacity development is most pressing from the LMIC perspective, where limited resources create an urgency to seek the most efficient trial design and method. The aim of this paper is to encourage broad debate about this complex area of research. By opening up this debate, GFBR aims to promote the appropriate and ethical use of novel designs and methods so their full potential to address the health needs in LMICs can be realised.

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BACKGROUND: Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve. METHODS: Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation - a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency. RESULTS: Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk. CONCLUSION: The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

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BACKGROUND: Cluster randomised trials (CRTs) are a key instrument to evaluate public health interventions. Fidelity assessment examines study processes to gauge whether an intervention was delivered as initially planned. Evaluation of implementation fidelity (IF) is required to establish whether the measured effects of a trial are due to the intervention itself and may be particularly important for CRTs of complex interventions conducted in low- and middle-income countries (LMICs). However, current CRT reporting guidelines offer no guidance on IF assessment. The objective of this review was to study current practices concerning the assessment of IF in CRTs of public health interventions in LMICs. METHODS: CRTs of public health interventions in LMICs that planned or reported IF assessment in either the trial protocol or the main trial report were included. The MEDLINE/PubMed, CINAHL and EMBASE databases were queried from January 2012 to May 2016. To ensure availability of a study protocol, CRTs reporting a registration number in the abstract were included. Relevant data were extracted from each study protocol and trial report by two researchers using a predefined screening sheet. Risk of bias for individual studies was assessed. RESULTS: We identified 90 CRTs of public health interventions in LMICs with a study protocol in a publicly available trial registry published from January 2012 to May 2016. Among these 90 studies, 25 (28%) did not plan or report assessing IF; the remaining 65 studies (72%) addressed at least one IF dimension. IF assessment was planned in 40% (36/90) of trial protocols and reported in 71.1% (64/90) of trial reports. The proportion of overall agreement between the trial protocol and trial report concerning occurrence of IF assessment was 66.7% (60/90). Most studies had low to moderate risk of bias. CONCLUSIONS: IF assessment is not currently a systematic practice in CRTs of public health interventions carried out in LMICs. In the absence of IF assessment, it may be difficult to determine if CRT results are due to the intervention design, to its implementation, or to unknown or external factors that may influence results. CRT reporting guidelines should promote IF assessment. TRIAL REGISTRATION: Protocol published and available at: https://doi.org/10.1186/s13643-016-0351-0.

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BACKGROUND: Treatment non-adherence in randomised trials refers to situations where some participants do not receive their allocated treatment as intended. For cluster randomised trials, where the unit of randomisation is a group of participants, non-adherence may occur at the cluster or individual level. When non-adherence occurs, randomisation no longer guarantees that the relationship between treatment receipt and outcome is unconfounded, and the power to detect the treatment effects in intention-to-treat analysis may be reduced. Thus, recording adherence and estimating the causal treatment effect adequately are of interest for clinical trials. OBJECTIVES: To assess the extent of reporting of non-adherence issues in published cluster trials and to establish which methods are currently being used for addressing non-adherence, if any, and whether clustering is accounted for in these. METHODS: We systematically reviewed 132 cluster trials published in English in 2011 previously identified through a search in PubMed. RESULTS: One-hundred and twenty three cluster trials were included in this systematic review. Non-adherence was reported in 56 cluster trials. Among these, 19 reported a treatment efficacy estimate: per protocol in 15 and as treated in 4. No study discussed the assumptions made by these methods, their plausibility or the sensitivity of the results to deviations from these assumptions. LIMITATIONS: The year of publication of the cluster trials included in this review (2011) could be considered a limitation of this study; however, no new guidelines regarding the reporting and the handling of non-adherence for cluster trials have been published since. In addition, a single reviewer undertook the data extraction. To mitigate this, a second reviewer conducted a validation of the extraction process on 15 randomly selected reports. Agreement was satisfactory (93%). CONCLUSION: Despite the recommendations of the Consolidated Standards of Reporting Trials statement extension to cluster randomised trials, treatment adherence is under-reported. Among the trials providing adherence information, there was substantial variation in how adherence was defined, handled and reported. Researchers should discuss the assumptions required for the results to be interpreted causally and whether these are scientifically plausible in their studies. Sensitivity analyses to study the robustness of the results to departures from these assumptions should be performed.

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BACKGROUND: The capacity for health systems to support the translation of research in to clinical practice may be limited. The cluster randomised controlled trial (cluster RCT) design is often employed in evaluating the effectiveness of implementation of evidence-based practices. We aimed to systematically review available evidence to identify and evaluate the components in the implementation process at the facility level using cluster RCT designs. METHODS: All cluster RCTs where the healthcare facility was the unit of randomisation, published or written from 1990 to 2014, were assessed. Included studies were analysed for the components of implementation interventions employed in each. Through iterative mapping and analysis, we synthesised a master list of components used and summarised the effects of different combinations of interventions on practices. RESULTS: Forty-six studies met the inclusion criteria and covered the specialty groups of obstetrics and gynaecology (n=9), paediatrics and neonatology (n=4), intensive care (n=4), internal medicine (n=20), and anaesthetics and surgery (n=3). Six studies included interventions that were delivered across specialties. Nine components of multifaceted implementation interventions were identified: leadership, barrier identification, tailoring to the context, patient involvement, communication, education, supportive supervision, provision of resources, and audit and feedback. The four main components that were most commonly used were education (n=42, 91%), audit and feedback (n=26, 57%), provision of resources (n=23, 50%) and leadership (n=21, 46%). CONCLUSIONS: Future implementation research should focus on better reporting of multifaceted approaches, incorporating sets of components that facilitate the translation of research into practice, and should employ rigorous monitoring and evaluation.