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BackgroundDue 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.MethodsBased 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.FindingsReductions 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).InterpretationWhile 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.FundingThis study was supported by Novartis Foundation (A122666), the Bill & Melinda Gates Foundation (OPP1160129), and the Horchow Family Fund (5,300,375,400).

Abstract 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.

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

Summary: 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).

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Abstract 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

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. [ABSTRACT FROM AUTHOR]

Abstract Background Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results. Methods A systematic review of CRTs containing spatial methods, defined as a method that accounts for the structure, location, or relative distances between observations. We searched three sources: Ovid/Medline, Pubmed, and Web of Science databases. Spatial methods were categorised and details of the impact of spatial effects on trial results recorded. Results We identified ten papers which met the inclusion criteria, comprising thirteen trials. We found that existing approaches fell into two categories; spatial variables and spatial modelling. The spatial variable approach was most common and involved standard statistical analysis of distance measurements. Spatial modelling is a more sophisticated approach which incorporates the spatial structure of the data within a random effects model. Studies tended to demonstrate the importance of accounting for location and distribution of observations in estimating unbiased effects. Conclusions There have been a few attempts to control and estimate spatial effects within the context of human CRTs, but our overall understanding is limited. Although spatial effects may bias trial results, their consideration was usually a supplementary, rather than primary analysis. Further work is required to evaluate and develop the spatial methodologies relevant to a range of CRTs.

Abstract Background Despite large-scale efforts to diagnose people living with HIV, 54% remain undiagnosed in sub-Saharan Africa. The gap in knowledge of HIV status and uptake of follow-on services remains wide with much lower rates of HIV testing among men compared to women. Here, we design a study to investigate the effect on uptake of HIV testing and linkage into care or prevention of partner-delivered HIV self-testing alone or with an additional intervention among male partners of pregnant women. Methods A phase II, adaptive, multi-arm, multi-stage cluster randomised trial, randomising antenatal clinic (ANC) days to six different trial arms. Pregnant women accessing ANC in urban Malawi for the first time will be recruited into either the standard of care (SOC) arm (invitation letter to the male partner offering HIV testing) or one of five intervention arms offering oral HIV self-test kits. Three of the five intervention arms will additionally offer the male partner a financial incentive (fixed or lottery amount) conditional on linkage after self-testing with one arm testing phone call reminders. Assuming that 25% of male partners link to care or prevention in the SOC arm, six clinic days, with a harmonic mean of 21 eligible participants, per arm will provide 80% power to detect a 0.15 absolute difference in the primary outcome. Cluster proportions will be analysed by a cluster summaries approach with adjustment for clustering and multiplicity. Discussion This trial applies adaptive methods which are novel and efficient designs. The methodology and lessons learned here will be important as proof of concept of how to design and conduct similar studies in the future. Although small, this trial will potentially present good evidence on the type of effective interventions for improving linkage into ART or prevention. The trial results will also have important policy implications on how to implement HIVST targeting male partners of pregnant women who are accessing ANC for the first time while paying particular attention to safety concerns. Contamination may occur if women in the intervention arms share their self-test kits with women in the SOC arm. Trial registration ISRCTN, ID: 18421340 . Registered on 31 March 2016.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many stepped wedge trials (SWTs) are analysed by using a mixed-effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common-to-all or varied-between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within-cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within-cluster comparisons in the standard model. In the SWTs simulated here, mixed-effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within-cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Background: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results. Methods: A systematic review of CRTs containing spatial methods, defined as a method that accounts for the structure, location, or relative distances between observations. We searched three sources: Ovid/Medline, Pubmed, and Web of Science databases. Spatial methods were categorised and details of the impact of spatial effects on trial results recorded. Results: We identified ten papers which met the inclusion criteria, comprising thirteen trials. We found that existing approaches fell into two categories; spatial variables and spatial modelling. The spatial variable approach was most common and involved standard statistical analysis of distance measurements. Spatial modelling is a more sophisticated approach which incorporates the spatial structure of the data within a random effects model. Studies tended to demonstrate the importance of accounting for location and distribution of observations in estimating unbiased effects. Conclusions: There have been a few attempts to control and estimate spatial effects within the context of human CRTs, but our overall understanding is limited. Although spatial effects may bias trial results, their consideration was usually a supplementary, rather than primary analysis. Further work is required to evaluate and develop the spatial methodologies relevant to a range of CRTs. [ABSTRACT FROM AUTHOR]

Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline covariate-adjusted cluster-level analysis, random effects logistic regression and generalised estimating equations when binary outcomes are missing under a baseline covariate-dependent missingness mechanism. Missing outcomes were handled using complete records analysis and multilevel multiple imputation. We analytically show that cluster-level analyses for estimating risk ratio using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same covariate effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline covariate in the outcome model. On the basis of the simulation study and analytical results, we give guidance on the conditions under which each approach is valid. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Background: Despite large-scale efforts to diagnose people living with HIV, 54% remain undiagnosed in sub-Saharan Africa. The gap in knowledge of HIV status and uptake of follow-on services remains wide with much lower rates of HIV testing among men compared to women. Here, we design a study to investigate the effect on uptake of HIV testing and linkage into care or prevention of partner-delivered HIV self-testing alone or with an additional intervention among male partners of pregnant women.Methods: A phase II, adaptive, multi-arm, multi-stage cluster randomised trial, randomising antenatal clinic (ANC) days to six different trial arms. Pregnant women accessing ANC in urban Malawi for the first time will be recruited into either the standard of care (SOC) arm (invitation letter to the male partner offering HIV testing) or one of five intervention arms offering oral HIV self-test kits. Three of the five intervention arms will additionally offer the male partner a financial incentive (fixed or lottery amount) conditional on linkage after self-testing with one arm testing phone call reminders. Assuming that 25% of male partners link to care or prevention in the SOC arm, six clinic days, with a harmonic mean of 21 eligible participants, per arm will provide 80% power to detect a 0.15 absolute difference in the primary outcome. Cluster proportions will be analysed by a cluster summaries approach with adjustment for clustering and multiplicity.Discussion: This trial applies adaptive methods which are novel and efficient designs. The methodology and lessons learned here will be important as proof of concept of how to design and conduct similar studies in the future. Although small, this trial will potentially present good evidence on the type of effective interventions for improving linkage into ART or prevention. The trial results will also have important policy implications on how to implement HIVST targeting male partners of pregnant women who are accessing ANC for the first time while paying particular attention to safety concerns. Contamination may occur if women in the intervention arms share their self-test kits with women in the SOC arm.Trial Registration: ISRCTN, ID: 18421340 . Registered on 31 March 2016. [ABSTRACT FROM AUTHOR]

Background: Cluster randomised trials (CRTs) are a key instrument to evaluate public health interventions, particularly in low- and middle-income countries (LMICs). 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. Current CRT reporting guidelines offer no guidance on IF assessment. We will systematically review the scientific literature to study current practices concerning the assessment of IF in CRTs of public health interventions in LMICs. Methods: We will include CRTs of public health interventions in LMICs that planned or assessed IF in either the trial protocol or the main trial report (or an associated document). Search strategies use Medical Subject Headings (MESH) and text words related to CRTs, developing countries, and public health interventions. The electronic database search was developed first for MEDLINE and adapted for the following databases: EMBASE, CINAHL, PubMed, and EMB Reviews, to identify CRT reports in English, Spanish, or French published on or after January 1, 2012. To ensure availability of a study protocol, we will include CRTs reporting a registration number in the abstract. For each included study, we will compare planned versus reported assessment of IF, and consider the dimensions of IF studied, and data collection methods used to evaluate each dimension. Data will be synthesised using quantitative and narrative techniques. Risk of bias for individual studies will be assessed using the Cochrane Collaboration Risk of Bias Tool criteria and additional criteria related to CRT methods. We will investigate possible sources of heterogeneity by performing subgroup analysis. This review was not eligible for inclusion in the PROSPERO registry. Discussion: Fidelity assessment may be a key tool for making studies more reliable, internally valid, and externally generalizable. This review will provide a portrait of current practices related to the assessment of intervention fidelity in CRTs and offer suggestions for improvement. Results will be relevant to researchers, those who finance health interventions, and for decision-makers who seek the best evidence on public health interventions. [ABSTRACT FROM AUTHOR]

Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design. Across all the missing data mechanisms considered, the multiple imputation methods provided estimators with negligible bias, while complete case analysis resulted in biased treatment effect estimates in scenarios where the randomised treatment arm was associated with missingness. Confidence interval coverage was generally in excess of nominal levels (up to 99.8%) following fixed-effects multiple imputation and too low following single-level multiple imputation. Multilevel multiple imputation led to coverage levels of approximately 95% throughout. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Background: Cluster randomised trials (CRTs) are commonly analysed using mixed-effects models or generalised estimating equations (GEEs). However, these analyses do not always perform well with the small number of clusters typical of most CRTs. They can lead to increased risk of a type I error (finding a statistically significant treatment effect when it does not exist) if appropriate corrections are not used.Methods: We conducted a small simulation study to evaluate the impact of using small-sample corrections for mixed-effects models or GEEs in CRTs with a small number of clusters. We then reanalysed data from TRIGGER, a CRT with six clusters, to determine the effect of using an inappropriate analysis method in practice. Finally, we reviewed 100 CRTs previously identified by a search on PubMed in order to assess whether trials were using appropriate methods of analysis. Trials were classified as at risk of an increased type I error rate if they did not report using an analysis method which accounted for clustering, or if they had fewer than 40 clusters and performed an individual-level analysis without reporting the use of an appropriate small-sample correction.Results: Our simulation study found that using mixed-effects models or GEEs without an appropriate correction led to inflated type I error rates, even for as many as 70 clusters. Conversely, using small-sample corrections provided correct type I error rates across all scenarios. Reanalysis of the TRIGGER trial found that inappropriate methods of analysis gave much smaller P values (P ≤ 0.01) than appropriate methods (P = 0.04-0.15). In our review, of the 99 trials that reported the number of clusters, 64 (65 %) were at risk of an increased type I error rate; 14 trials did not report using an analysis method which accounted for clustering, and 50 trials with fewer than 40 clusters performed an individual-level analysis without reporting the use of an appropriate correction.Conclusions: CRTs with a small or medium number of clusters are at risk of an inflated type I error rate unless appropriate analysis methods are used. Investigators should consider using small-sample corrections with mixed-effects models or GEEs to ensure valid results. [ABSTRACT FROM AUTHOR]

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size. [ABSTRACT FROM AUTHOR]

Background Previous reviews of cluster randomised trials have been critical of the quality of the trials reviewed, but none has explored determinants of the quality of these trials in a specific field over an extended period of time. Recent work suggests that correct conduct and reporting of these trials may require more than published guidelines. In this review, our aim was to assess the quality of cluster randomised trials conducted in residential facilities for older people, and to determine whether (1) statistician involvement in the trial and (2) strength of journal endorsement of the Consolidated Standards of Reporting Trials (CONSORT) statement influence quality. Methods We systematically identified trials randomising residential facilities for older people, or parts thereof, without language restrictions, up to the end of 2010, using National Library of Medicine (Medline) via PubMed and hand-searching. We based quality assessment criteria largely on the extended CONSORT statement for cluster randomised trials. We assessed statistician involvement based on statistician co-authorship, and strength of journal endorsement of the CONSORT statement from journal websites. Results 73 trials met our inclusion criteria. Of these, 20 (27%) reported accounting for clustering in sample size calculations and 54 (74%) in the analyses. In 29 trials (40%), methods used to identify/recruit participants were judged by us to have potentially caused bias or reporting was unclear to reach a conclusion. Some elements of quality improved over time but this appeared not to be related to the publication of the extended CONSORT statement for these trials. Trials with statistician/epidemiologist co-authors were more likely to account for clustering in sample size calculations (unadjusted odds ratio 5.4, 95% confidence interval 1.1 to 26.0) and analyses (unadjusted OR 3.2, 1.2 to 8.5). Journal endorsement of the CONSORT statement was not associated with trial quality. Conclusions Despite international attempts to improve methods in cluster randomised trials, important quality limitations remain amongst these trials in residential facilities. Statistician involvement on trial teams may be more effective in promoting quality than further journal endorsement of the CONSORT statement. Funding bodies and journals should promote statistician involvement and co-authorship in addition to adherence to CONSORT guidelines. [ABSTRACT FROM AUTHOR]

SUMMARY Statistical methods have been developed for cost-effectiveness analyses of cluster randomised trials (CRTs) where baseline covariates are balanced. However, CRTs may show systematic differences in individual and cluster-level covariates between the treatment groups. This paper presents three methods to adjust for imbalances in observed covariates: seemingly unrelated regression with a robust standard error, a 'two-stage' bootstrap approach combined with seemingly unrelated regression and multilevel models. We consider the methods in a cost-effectiveness analysis of a CRT with covariate imbalance, unequal cluster sizes and a prognostic relationship that varied by treatment group. The cost-effectiveness results differed according to the approach for covariate adjustment. A simulation study then assessed the relative performance of methods for addressing systematic imbalance in baseline covariates. The simulations extended the case study and considered scenarios with different levels of confounding, cluster size variation and few clusters. Performance was reported as bias, root mean squared error and CI coverage of the incremental net benefit. Even with low levels of confounding, unadjusted methods were biased, but all adjusted methods were unbiased. Multilevel models performed well across all settings, and unlike the other methods, reported CI coverage close to nominal levels even with few clusters of unequal sizes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

AbstractTrials of educational or organizational interventions to change clinical practice require cluster randomization, that is, randomization of units such as hospitals or clinical teams rather than individual patients. Cluster randomization is relatively novel in health care settings and raises new methodological challenges, in particular: are units willing to be randomized at an organizational level; and, what procedures should be followed to successfully enrol all of the clinicians in a unit rather than individual clinicians as in conventional multicentre trials. This is particularly problematic for trials of large units such as hospitals. The aim of this study was to develop and partially evaluate a strategy to recruit acute, secondary care NHS hospitals in the UK into cluster randomized trials. Literature search and interviews with senior staff in acute hospitals and relevant national organizations were used to develop a recruitment strategy. The strategy was evaluated by inviting 32 randomly selected clinical directorates to participate in a trial feasibility study. A seven step recruitment strategy was developed: (1) Identify stakeholders and gatekeepers; (2) Inform stakeholders and gatekeepers; (3) Approach gatekeepers; (4) Local negotiation; (5) Conduct the research; (6) Feedback to gatekeepers; (7) Feedback to stakeholders. Key problems were the possibility of multiple gatekeepers and identification of all possible stakeholders in varying organizational structures. The strategy was effective in two respects. First, 32 (100%) of the directorates approached agreed to participate. Second, baseline data collection was successfully achieved in all of the directorates. However, the strategy is costly in terms of time and resources. We conclude that NHS trusts are willing to participate in cluster randomized trials. This recruitment strategy is successful and could be widely adopted, but realistic time and financial cost estimates are required at the planning stage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.