Contamination in trials: is cluster randomisation the answer?
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ABSTRACT:
SUBMITTER: Torgerson DJ
PROVIDER: S-EPMC1119583 | biostudies-literature | 2001 Feb
REPOSITORIES: biostudies-literature
Publications
Contamination in trials: is cluster randomisation the answer?
BMJ (Clinical research ed.) 20010201 7282
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Project description:BackgroundCluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each.MethodsWe describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand.ResultsCRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as 'informative cluster size'), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present.ConclusionWe conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity.
| S-EPMC9908044 | biostudies-literature
Project description:BackgroundIn cluster randomized trials (CRTs) of interventions against malaria, mosquito movement between households ultimately leads to contamination between intervention and control arms, unless they are separated by wide buffer zones.MethodsThis paper proposes a method for adjusting estimates of intervention effectiveness for contamination and for estimating a contamination range between intervention arms, the distance over which contamination measurably biases the estimate of effectiveness. A sigmoid function is fitted to malaria prevalence or incidence data as a function of the distance of households to the intervention boundary, stratified by intervention status and including a random effect for the clustering. The method is evaluated in a simulation study, corresponding to a range of rural settings with varying intervention effectiveness and contamination range, and applied to a CRT of insecticide treated nets in Ghana.ResultsThe simulations indicate that the method leads to approximately unbiased estimates of effectiveness. Precision decreases with increasing mosquito movement, but the contamination range is much smaller than the maximum distance traveled by mosquitoes. For the method to provide precise and approximately unbiased estimates, at least 50% of the households should be at distances greater than the estimated contamination range from the discordant intervention arm.ConclusionsA sigmoid approach provides an appropriate analysis for a CRT in the presence of contamination. Outcome data from boundary zones should not be discarded but used to provide estimates of the contamination range. This gives an alternative to "fried egg" designs, which use large clusters (increasing costs) and exclude buffer zones to avoid bias.
| S-EPMC8434732 | biostudies-literature
Project description:BackgroundCluster randomised trials often randomise a small number of units, putting them at risk of poor balance of covariates across treatment arms. Covariate constrained randomisation aims to reduce this risk by removing the worst balanced allocations from consideration. This is known to provide only a small gain in power over that averaged under simple randomisation and is likely influenced by the number and prognostic effect of the covariates. We investigated the performance of covariate constrained randomisation in comparison to the worst balanced allocations, and considered the impact on the power of the prognostic effect and number of covariates adjusted for in the analysis.MethodsUsing simulation, we examined the Monte Carlo type I error rate and power of cross-sectional, two-arm parallel cluster-randomised trials with a continuous outcome and four binary cluster-level covariates, using either simple or covariate constrained randomisation. Data were analysed using a small sample corrected linear mixed-effects model, adjusted for some or all of the binary covariates. We varied the number of clusters, intra-cluster correlation, number and prognostic effect of covariates balanced in the randomisation and adjusted in the analysis, and the size of the candidate set from which the allocation was selected. For each scenario, 20,000 simulations were conducted.ResultsWhen compared to the worst balanced allocations, covariate constrained randomisation with an adjusted analysis provided gains in power of up to 20 percentage points. Even with analysis-based adjustment for those covariates balanced in the randomisation, the type I error rate was not maintained when the intracluster correlation is very small (0.001). Generally, greater power was achieved when more prognostic covariates are restricted in the randomisation and as the size of the candidate set decreases. However, adjustment for weakly prognostic covariates lead to a loss in power of up to 20 percentage points.ConclusionsWhen compared to the worst balanced allocations, covariate constrained randomisation provides moderate to substantial improvements in power. However, the prognostic effect of the covariates should be carefully considered when selecting them for inclusion in the randomisation.
| S-EPMC9006416 | biostudies-literature
Project description:BACKGROUND: Although guidelines for the care of the dying patient exist the evidence base to support the guidelines is poor. Some of the factors contributing to this include failure to recruit to trials, protective healthcare professionals and subsequent attrition from trials due to the death of the patients. Recent studies report favourably on the use of cluster randomisation as an appropriate methodology for use in this patient group. METHODS/DESIGN: A feasibility study, exploring two types of randomisation as appropriate methodology for trials involving dying patients. Cluster randomisation and randomised consent will be utilised following a crossover design at two sites, one oncology ward and one Macmillan unit within the Northwest Wales NHS Trust. All patients commencing on the Integrated Care Pathway (ICP) for the Last Days of Life will be eligible for inclusion in the study. Using the hypothesis that it is not necessary to prescribe an anti-emetic medication when setting up a syringe driver for the dying patient, the study will evaluate different models of research methodology. DISCUSSION: The identification of the most appropriate methodology for use in studies concerning this patient group will inform the development of future clinical studies. Furthermore, the outcomes of this feasibility study will inform the development, of a proposal seeking funding for Wales-wide trials in palliative care. The identification of an appropriate methodology will provide a starting point for the establishment of a robust evidence base for the care of the dying patient.
| S-EPMC420454 | biostudies-literature
Project description:Randomised trials in emergency settings must quickly confirm eligibility and allocate participants to an intervention group without delaying treatment. We report rapid randomisation during two neonatal resuscitation trials using the non-commercial REDCap platform accessed via smartphone. This simple, reliable method has wide applicability for trials in emergency settings. What is Known: • Randomised trials in emergency settings need to rapidly allocate participants to an intervention group. • This process should not delay treatment. What is New: • This non-commercial, smartphone-accessible application enabled rapid, accurate randomisation at the bedside. • This has broad applicability for emergency setting trials.
| S-EPMC9352638 | biostudies-literature
Project description:BackgroundIn healthcare research the randomised controlled trial is seen as the gold standard because it ensures selection bias is minimised. However, there is uncertainty as to which is the most preferred method of randomisation in any given setting and to what extent more complex methods are actually being implemented in the field.MethodsIn this paper we describe the results of a survey of UK academics and publicly funded researchers to examine the extent of the use of various methods of randomisation in clinical trials.ResultsTrialists reported using simple randomisation, permuted blocks and stratification more often than more complex methods such as minimisation. Most trialists believed that simple randomisation is suitable for larger trials but there is a high probability of possible imbalance between treatment groups in small trials. It was thought that groups should be balanced at baseline to avoid imbalance and help face-validity. However, very few respondents considered that more complex methods offer any advantages.ConclusionsThis paper demonstrates that for most UK trialists the preferred method of randomisation is using permuted blocks of varying random length within strata. This method eliminates the problem of predictability while maintaining balance across combinations of factors. If the number of prognostic factors is large, then minimisation can be used to provide treatment balance as well as balance over these factors. However, only those factors known to affect outcome should be considered.
| S-EPMC3522058 | biostudies-literature
Project description: Not available
| S-EPMC10953118 | biostudies-literature
Project description:Often patients may require treatment on multiple occasions. The re-randomisation design can be used in such multi-episode settings, as it allows patients to be re-enrolled and re-randomised for each new treatment episode they experience. We propose a set of estimands that can be used in multi-episode settings, focusing on issues unique to multi-episode settings, namely how each episode should be weighted, how the patient's treatment history in previous episodes should be handled, and whether episode-specific effects or average effects across all episodes should be used. We then propose independence estimators for each estimand, and show the manner in which many re-randomisation trials have been analysed in the past (a simple comparison between all intervention episodes vs. all control episodes) corresponds to a per-episode added-benefit estimand, that is, the average effect of the intervention across all episodes, over and above any benefit conferred from the intervention in previous episodes. We show this estimator is generally unbiased, and describe when other estimators will be unbiased. We conclude that (i) consideration of these estimands can help guide the choice of which analysis method is most appropriate; and (ii) the re-randomisation design with an independence estimator can be a useful approach in multi-episode settings.
| S-EPMC9251752 | biostudies-literature
Project description:BackgroundIn a non-inferiority (NI) trial, analysis based on the intention-to-treat (ITT) principle is anti-conservative, so current guidelines recommend analysing on a per-protocol (PP) population in addition. However, PP analysis relies on the often implausible assumption of no confounders. Randomisation-based efficacy estimators (RBEEs) allow for treatment non-adherence while maintaining a comparison of randomised groups. Fischer et al. have developed an approach for estimating RBEEs in randomised trials with two active treatments, a common feature of NI trials. The aim of this paper was to demonstrate the use of RBEEs in NI trials using this approach, and to appraise the feasibility of these estimators as the primary analysis in NI trials.MethodsTwo NI trials were used. One comparing two different dosing regimens for the maintenance of remission in people with ulcerative colitis (CODA), and the other comparing an orally administered treatment to an intravenously administered treatment in preventing skeletal-related events in patients with bone metastases from breast cancer (ZICE). Variables that predicted adherence in each of the trial arms, and were also independent of outcome, were sought in each of the studies. Structural mean models (SMMs) were fitted that conditioned on these variables, and the point estimates and confidence intervals compared to that found in the corresponding ITT and PP analyses.ResultsIn the CODA study, no variables were found that differentially predicted treatment adherence while remaining independent of outcome. The SMM, using standard methodology, moved the point estimate closer to 0 (no difference between arms) compared to the ITT and PP analyses, but the confidence interval was still within the NI margin, indicating that the conclusions drawn would remain the same. In the ZICE study, cognitive functioning as measured by the corresponding domain of the QLQ-C30, and use of chemotherapy at baseline were both differentially associated with adherence while remaining independent of outcome. However, while the SMM again moved the point estimate closer to 0, the confidence interval was wide, overlapping with any NI margin that could be justified.ConclusionDeriving RBEEs in NI trials with two active treatments can provide a randomisation-respecting estimate of treatment efficacy that accounts for treatment adherence, is straightforward to implement, but requires thorough planning during the design stage of the study to ensure that strong baseline predictors of treatment are captured. Extension of the approach to handle nonlinear outcome variables is also required.Trial registrationThe CODA study: ClinicalTrials.gov, identifier: NCT00708656 . Registered on 8 April 2008. The ZICE study trial: ClinicalTrials.gov, identifier: NCT00326820 . Registered on 16 May 2006.
| S-EPMC5343391 | biostudies-literature
Project description:BackgroundIn cluster randomized crossover (CRXO) trials, groups of participants (i.e., clusters) are randomly allocated to receive a sequence of interventions over time (i.e., cluster periods). CRXO trials are becoming more comment when they are feasible, as they require fewer clusters than parallel group cluster randomized trials. However, CRXO trials have not been frequently used in orthopedic fracture trials and represent a novel methodological application within the field. To disseminate the early knowledge gained from our experience initiating two cluster randomized crossover trials, we describe our process for the identification and selection of the orthopedic practices (i.e., clusters) participating in the PREP-IT program and present data to describe their key characteristics.MethodsThe PREP-IT program comprises two ongoing pragmatic cluster randomized crossover trials (Aqueous-PREP and PREPARE) which compare the effect of iodophor versus chlorhexidine solutions on surgical site infection and unplanned fracture-related reoperations in patients undergoing operative fracture management. We describe the process we used to identify and select orthopedic practices (clusters) for the PREP-IT trials, along with their characteristics.ResultsWe identified 58 potential orthopedic practices for inclusion in the PREP-IT trials. After screening each practice for eligibility, we selected 30 practices for participation and randomized each to a sequence of interventions (15 for Aqueous-PREP and 20 for PREPARE). The majority of orthopedic practices included in the Aqueous-PREP and PREPARE trials were situated in level I trauma centers (100% and 87%, respectively). Orthopedic practices in the Aqueous-PREP trial operatively treated a median of 149 open fracture patients per year, included a median of 11 orthopedic surgeons, and had access to a median of 5 infection preventionists. Orthopedic practices in the PREPARE trial treated a median of 142 open fracture and 1090 closed fracture patients per year, included a median of 7.5 orthopedic surgeons, and had access to a median of 6 infection preventionists.ConclusionsThe PREP-IT trials provide an example of how to follow the reporting standards for cluster randomized crossover trials by providing a clear definition of the cluster unit, a thorough description of the cluster identification and selection process, and sufficient description of key cluster characteristics.Trial registrationBoth trials are registered at ClinicalTrials.gov (A-PREP: NCT03385304 December 28, 2017, and PREPARE: NCT03523962 May 14, 2018).
| S-EPMC7425374 | biostudies-literature