Why and when should we cluster randomize?

A cluster randomized trial is defined as a randomized trial in which intact social units of individuals are randomized rather than individuals themselves. Outcomes are observed on individual participants within clusters (such as patients). Such a design allows assessing interventions targeting cluster-level participants (such as physicians), individual participants or both. Indeed, many interventions assessed in cluster randomized trials are actually complex ones, with distinct components targeting different levels. For a cluster-level intervention, cluster randomization is an obvious choice: the intervention is not divisible at the individual-level. For individual-level interventions, cluster randomization may nevertheless be suitable to prevent group contamination, for logistical reasons, to enhance participants' adherence, or when objectives pertain to the cluster level. An unacceptable reason for cluster randomization would be to avoid obtaining individual consent. Indeed, participants in cluster randomized trials have to be protected as in any type of trial design. Participants may be people from whom data are collected, but they may also be people who are intervened upon, and this includes both patients and physicians (for example, physicians receiving training interventions). Consent should be sought as soon as possible, although there may exist situations where participants may consent only for data collection, not for being exposed to the intervention (because, for instance, they cannot opt-out). There may even be situations where participants are not able to consent at all. In this latter situation a waiver of consent must be granted by a research ethics committee.

Heterogeneity in pragmatic randomised trials: sources and management.

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.

An analysis of published trials found that current use of pragmatic trial labels is uninformative.

OBJECTIVES: Randomized trials labelled as "pragmatic" are attractive to funders, patients, and clinicians as the label implies that the results are directly applicable to clinical care. We examined how authors justify use of the label (e.g., by referring to one or more PRECIS [PRagmatic Explanatory Continuum Indicator Summary]-2 domains). STUDY DESIGN AND SETTING: We reviewed primary trial reports published 2014-2019, registered in ClinicalTrials.gov and using the pragmatic label anywhere in the report. RESULTS: Among 415 trials, the label was justified by reference to at least one design element in 282 (68.0%); of these, 240 (85.1%) referenced trial characteristics that can be mapped to one or more of the PRECIS-2 domains, most commonly eligibility (91, 32.3%), setting (90, 31.9%), flexibility delivery (89, 31.6%), and organization (75, 26.6%); 42 (14.9%) referenced characteristics that are not PRECIS-2 domains, most commonly type of intervention/comparator (48, 17%), recruitment without consent (22, 7.8%), routinely collected data (22, 7.8%), and cluster randomization (20, 7.1%). Most reports referenced only one or two design elements. Overall, 9/415 (2.2%) provided PRECIS wheels. CONCLUSION: Current use of pragmatic labels is uninformative. Authors should clarify the decision the trial is intended to support and include a PRECIS-2 table to make the design transparent.

Pilot and feasibility studies for pragmatic trials have unique considerations and areas of uncertainty.

BACKGROUND AND OBJECTIVE: Feasibility studies are increasingly being used to support the development of, and investigate uncertainties around, future large-scale trials. The future trial can be designed with either a pragmatic or explanatory mindset. Whereas pragmatic trials aim to inform the choice between different care options and thus, are designed to resemble conditions outside of a clinical trial environment, explanatory trials examine the benefit of a treatment under more controlled conditions. There is existing guidance for designing feasibility studies, but none that explicitly considers the goals of pragmatic designs. We aimed to identify unique areas of uncertainty that are relevant to planning a pragmatic trial. RESULTS: We identified ten relevant domains, partly based on the pragmatic-explanatory continuum indicator summary-2 (PRECIS-2) framework, and describe potential questions of uncertainty within each: intervention development, research ethics, participant identification and eligibility, recruitment of individuals, setting, organization, flexibility of delivery, flexibility of adherence, follow-up, and importance of primary outcome to patients and decision-makers. We present examples to illustrate how uncertainty in these domains might be addressed within a feasibility study. CONCLUSION: Researchers planning a feasibility study in advance of a pragmatic trial should consider feasibility objectives specifically relevant to areas of uncertainty for pragmatic trials.

Association of intracluster correlation measures with outcome prevalence for binary outcomes in cluster randomised trials.

In cluster randomised trials, a measure of intracluster correlation such as the intraclass correlation coefficient (ICC) should be reported for each primary outcome. Providing intracluster correlation estimates may help in calculating sample size of future cluster randomised trials and also in interpreting the results of the trial from which they are derived. For a binary outcome, the ICC is known to be associated with its prevalence, which raises at least two issues. First, it questions the use of ICC estimates obtained on a binary outcome in a trial for sample size calculations in a subsequent trial in which the same binary outcome is expected to have a different prevalence. Second, it challenges the interpretation of ICC estimates because they do not solely depend on clustering level. Other intracluster correlation measures proposed for clustered binary data settings include the variance partition coefficient, the median odds ratio and the tetrachoric correlation coefficient. Under certain assumptions, the theoretical maximum possible value for an ICC associated with a binary outcome can be derived, and we proposed the relative deviation of an ICC estimate to this maximum value as another measure of the intracluster correlation. We conducted a simulation study to explore the dependence of these intracluster correlation measures on outcome prevalence and found that all are associated with prevalence. Even if all depend on prevalence, the tetrachoric correlation coefficient computed with Kirk's approach was less dependent on the outcome prevalence than the other measures when the intracluster correlation was about 0.05. We also observed that for lower values, such as 0.01, the analysis of variance estimator of the ICC is preferred.

Progression from external pilot to definitive randomised controlled trial: a methodological review of progression criteria reporting.

OBJECTIVES: Prespecified progression criteria can inform the decision to progress from an external randomised pilot trial to a definitive randomised controlled trial. We assessed the characteristics of progression criteria reported in external randomised pilot trial protocols and results publications, including whether progression criteria were specified a priori and mentioned in prepublication peer reviewer reports. STUDY DESIGN: Methodological review. METHODS: We searched four journals through PubMed: British Medical Journal Open, Pilot and Feasibility Studies, Trials and Public Library of Science One. Eligible publications reported external randomised pilot trial protocols or results, were published between January 2018 and December 2019 and reported progression criteria. We double data extracted 25% of the included publications. Here we report the progression criteria characteristics. RESULTS: We included 160 publications (123 protocols and 37 completed trials). Recruitment and retention were the most frequent indicators contributing to progression criteria. Progression criteria were mostly reported as distinct thresholds (eg, achieving a specific target; 133/160, 83%). Less than a third of the planned and completed pilot trials that included qualitative research reported how these findings would contribute towards progression criteria (34/108, 31%). The publications seldom stated who established the progression criteria (12/160, 7.5%) or provided rationale or justification for progression criteria (44/160, 28%). Most completed pilot trials reported the intention to proceed to a definitive trial (30/37, 81%), but less than half strictly met all of their progression criteria (17/37, 46%). Prepublication peer reviewer reports were available for 153/160 publications (96%). Peer reviewer reports for 86/153 (56%) publications mentioned progression criteria, with peer reviewers of 35 publications commenting that progression criteria appeared not to be specified. CONCLUSIONS: Many external randomised pilot trial publications did not adequately report or propose prespecified progression criteria to inform whether to proceed to a future definitive randomised controlled trial.

Transparency of informed consent in pilot and feasibility studies is inadequate: a single-center quality assurance study.

BACKGROUND: Pilot and feasibility studies (PAFS) often have complex objectives aimed at assessing feasibility of conducting a larger study. These may not be clear to participants in pilot studies. METHODS: Here, we aimed to assess the transparency of informed consent in PAFS by investigating whether researchers communicate, through patient information leaflets and consent forms, key features of the studies. We collected this data from original versions of these documents submitted for ethics approval and the final approved documents for PAFS submitted to the Hamilton Integrated Research Ethics Board, Canada. RESULTS: One hundred eighty-four PAFS, submitted for ethics approval from 2004 to 2020, were included, and we found that of the approved consent documents which were provided to participants, 83.2% (153) stated the terms "pilot" or "feasibility" in their title, 12% (22) stated the definition of a pilot/feasibility study, 42.4% (78) of the studies stated their intent to assess feasibility, 19.6% (36) stated the specific feasibility objectives, 1.6% (3) stated the criteria for success of the pilot study, and 0.5% (1) stated all five of these criteria. After ethics review, a small increase in transparency occurred, ranging from 1.6 to 2.8% depending on the criteria. By extracting data from the protocols of the PAFS, we found that 73.9% (136) stated intent to assess feasibility, 71.2% (131) stated specific feasibility objectives, and 33.7% (62) stated criteria for success of the study to lead to a larger study. CONCLUSION: The transparency of informed consent in PAFS is inadequate and needs to be specifically addressed by research ethics guidelines. Research ethics boards and researchers ought to be made aware and mindful of best practices of informed consent in the context of PAFS.

Assessing the transparency of informed consent in feasibility and pilot studies: a single-centre quality assurance study protocol.

INTRODUCTION: Pilot/feasibility studies assess the feasibility of conducting a larger study. Although researchers ought to communicate the feasibility objectives to their participants, many research ethics guidelines do not comment on how informed consent applies to pilot studies. It is unclear whether researchers and research ethics boards clearly communicate the purpose of pilot studies to participants consenting.The primary objective of this study is to assess whether pilot/feasibility studies submitted for ethics approval to a research ethics board transparently communicate the purpose of the study to participants through their informed consent practice. A highly transparent consent practice entails the consent documents communicate: (1) the term 'pilot' or 'feasibility' in the title; (2) the definition of a pilot/feasibility study; (3) the primary objectives of the study are to assess feasibility; (4) the specific feasibility objectives; and (5) the criteria for the study to successfully lead to the main study. The secondary objectives are to assess whether there is a difference between submitted and revised versions of the consent documents (revisions are made to obtain research ethics approval), to determine factors associated with transparent consent practices and to assess the consistency with which pilot and feasibility studies assess feasibility outcomes as their primary objectives. METHODS AND ANALYSIS: This is a retrospective review of informed consent information for pilot/feasibility studies submitted to the Hamilton integrated Research Ethics Board, Canada. We will look at submitted and revised consent documents for pilot/feasibility studies submitted over a 14-year period. We will use descriptive statistics to summarise data, reporting results as percentages with 95% CIs, and conduct logistic regression to determine characteristics associated with transparent consent practices. ETHICS AND DISSEMINATION: The study protocol was approved by the Hamilton integrated Research Ethics Board, and the results of this study will be submitted for publication in a peer-reviewed journal.

Cluster over individual randomization: are study design choices appropriately justified? Review of a random sample of trials.

BACKGROUND: Novel rationales for randomizing clusters rather than individuals appear to be emerging from the push for more pragmatic trials, for example, to facilitate trial recruitment, reduce the costs of research, and improve external validity. Such rationales may be driven by a mistaken perception that choosing cluster randomization lessens the need for informed consent. We reviewed a random sample of published cluster randomized trials involving only individual-level health care interventions to determine (a) the prevalence of reporting a rationale for the choice of cluster randomization; (b) the types of explicit, or if absent, apparent rationales for the use of cluster randomization; (c) the prevalence of reporting patient informed consent for study interventions; and (d) the types of justifications provided for waivers of consent. We considered cluster randomized trials for evaluating exclusively the individual-level health care interventions to focus on clinical trials where individual randomization is only theoretically possible and where there is a general expectation of informed consent. METHODS: A random sample of 40 cluster randomized trials were identified by implementing a validated electronic search filter in two electronic databases (Ovid MEDLINE and Embase), with two reviewers independently extracting information from each trial. Inclusion criteria were the following: primary report of a cluster randomized trial, evaluating exclusively an individual-level health care intervention, published between 2007 and 2016, and conducted in Canada, the United States, European Union, Australia, or low- and middle-income country settings. RESULTS: Twenty-five trials (62.5%, 95% confidence interval = 47.5%-77.5%) reported an explicit rationale for the use of cluster randomization. The most commonly reported rationales were those with logistical or administrative convenience (15 trials, 60%) and those that need to avoid contamination (13 trials, 52%); five trials (20%) were cited rationales related to the push for more pragmatic trials. Twenty-one trials (52.5%, 95% confidence interval = 37%-68%) reported written informed consent for the intervention, two (5%) reported verbal consent, and eight (20%) reported waivers of consent, while in nine trials (22.5%) consent was unclear or not mentioned. Reported justifications for waivers of consent included that study interventions were already used in clinical practice, patients were not randomized individually, and the need to facilitate the pragmatic nature of the trial. Only one trial reported an explicit and appropriate justification for waiver of consent based on minimum criteria in international research ethics guidelines, namely, infeasibility and minimal risk. CONCLUSION: Rationales for adopting cluster over individual randomization and for adopting consent waivers are emerging, related to the need to facilitate pragmatic trials. Greater attention to clear reporting of study design rationales, informed consent procedures, as well as justification for waivers is needed to ensure that such trials meet appropriate ethical standards.

Is the R coefficient of interest in cluster randomized trials with a binary outcome?

In cluster randomized trials, the intraclass correlation coefficient (ICC) is classically used to measure clustering. When the outcome is binary, the ICC is known to be associated with the prevalence of the outcome. This association challenges its interpretation and can be problematic for sample size calculation. To overcome these situations, Crespi et al. extended a coefficient named R, initially proposed by Rosner for ophthalmologic data, to cluster randomized trials. Crespi et al. asserted that R may be less influenced by the outcome prevalence than is the ICC, although the authors provided only empirical data to support their assertion. They also asserted that "the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required", although they did not consider empirical power. The aim of this study was to investigate whether R could indeed be considered independent of the outcome prevalence. We also considered whether sample size calculation should be better based on the R coefficient or the ICC. Considering the particular case of 2 individuals per cluster, we theoretically demonstrated that R is not symmetrical around the 0.5 prevalence value. This in itself demonstrates the dependence of R on prevalence. We also conducted a simulation study to explore the case of both fixed and variable cluster sizes greater than 2. This simulation study demonstrated that R decreases when prevalence increases from 0 to 1. Both the analytical and simulation results demonstrate that R depends on the outcome prevalence. In terms of sample size calculation, we showed that an approach based on the ICC is preferable to an approach based on the R coefficient because with the former, the empirical power is closer to the nominal one. Hence, the R coefficient does not outperform the ICC for binary outcomes because it does not offer any advantage over the ICC.

Ethical implications of excessive cluster sizes in cluster randomised trials.

The cluster randomised trial (CRT) is commonly used in healthcare research. It is the gold-standard study design for evaluating healthcare policy interventions. A key characteristic of this design is that as more participants are included, in a fixed number of clusters, the increase in achievable power will level off. CRTs with cluster sizes that exceed the point of levelling-off will have excessive numbers of participants, even if they do not achieve nominal levels of power. Excessively large cluster sizes may have ethical implications due to exposing trial participants unnecessarily to the burdens of both participating in the trial and the potential risks of harm associated with the intervention. We explore these issues through the use of two case studies. Where data are routinely collected, available at minimum cost and the intervention poses low risk, the ethical implications of excessively large cluster sizes are likely to be low (case study 1). However, to maximise the social benefit of the study, identification of excessive cluster sizes can allow for prespecified and fully powered secondary analyses. In the second case study, while there is no burden through trial participation (because the outcome data are routinely collected and non-identifiable), the intervention might be considered to pose some indirect risk to patients and risks to the healthcare workers. In this case study it is therefore important that the inclusion of excessively large cluster sizes is justifiable on other grounds (perhaps to show sustainability). In any randomised controlled trial, including evaluations of health policy interventions, it is important to minimise the burdens and risks to participants. Funders, researchers and research ethics committees should be aware of the ethical issues of excessively large cluster sizes in cluster trials.

Quality of reporting of pilot and feasibility cluster randomised trials: a systematic review.

OBJECTIVES: To systematically review the quality of reporting of pilot and feasibility of cluster randomised trials (CRTs). In particular, to assess (1) the number of pilot CRTs conducted between 1 January 2011 and 31 December 2014, (2) whether objectives and methods are appropriate and (3) reporting quality. METHODS: We searched PubMed (2011-2014) for CRTs with 'pilot' or 'feasibility' in the title or abstract; that were assessing some element of feasibility and showing evidence the study was in preparation for a main effectiveness/efficacy trial. Quality assessment criteria were based on the Consolidated Standards of Reporting Trials (CONSORT) extensions for pilot trials and CRTs. RESULTS: Eighteen pilot CRTs were identified. Forty-four per cent did not have feasibility as their primary objective, and many (50%) performed formal hypothesis testing for effectiveness/efficacy despite being underpowered. Most (83%) included 'pilot' or 'feasibility' in the title, and discussed implications for progression from the pilot to the future definitive trial (89%), but fewer reported reasons for the randomised pilot trial (39%), sample size rationale (44%) or progression criteria (17%). Most defined the cluster (100%), and number of clusters randomised (94%), but few reported how the cluster design affected sample size (17%), whether consent was sought from clusters (11%), or who enrolled clusters (17%). CONCLUSIONS: That only 18 pilot CRTs were identified necessitates increased awareness of the importance of conducting and publishing pilot CRTs and improved reporting. Pilot CRTs should primarily be assessing feasibility, avoiding formal hypothesis testing for effectiveness/efficacy and reporting reasons for the pilot, sample size rationale and progression criteria, as well as enrolment of clusters, and how the cluster design affects design aspects. We recommend adherence to the CONSORT extensions for pilot trials and CRTs.

Erratum to: Methods and processes for development of a CONSORT extension for reporting pilot randomized controlled trials.

[This corrects the article DOI: 10.1186/s40814-016-0065-z.].

CONSORT 2010 statement: extension to randomised pilot and feasibility trials.

The Consolidated Standards of Reporting Trials (CONSORT) statement is a guideline designed to improve the transparency and quality of the reporting of randomised controlled trials (RCTs). In this article we present an extension to that statement for randomised pilot and feasibility trials conducted in advance of a future definitive RCT. The checklist applies to any randomised study in which a future definitive RCT, or part of it, is conducted on a smaller scale, regardless of its design (eg, cluster, factorial, crossover) or the terms used by authors to describe the study (eg, pilot, feasibility, trial, study). The extension does not directly apply to internal pilot studies built into the design of a main trial, non-randomised pilot and feasibility studies, or phase II studies, but these studies all have some similarities to randomised pilot and feasibility studies and so many of the principles might also apply. The development of the extension was motivated by the growing number of studies described as feasibility or pilot studies and by research that has identified weaknesses in their reporting and conduct. We followed recommended good practice to develop the extension, including carrying out a Delphi survey, holding a consensus meeting and research team meetings, and piloting the checklist. The aims and objectives of pilot and feasibility randomised studies differ from those of other randomised trials. Consequently, although much of the information to be reported in these trials is similar to those in randomised controlled trials (RCTs) assessing effectiveness and efficacy, there are some key differences in the type of information and in the appropriate interpretation of standard CONSORT reporting items. We have retained some of the original CONSORT statement items, but most have been adapted, some removed, and new items added. The new items cover how participants were identified and consent obtained; if applicable, the prespecified criteria used to judge whether or how to proceed with a future definitive RCT; if relevant, other important unintended consequences; implications for progression from pilot to future definitive RCT, including any proposed amendments; and ethical approval or approval by a research review committee confirmed with a reference number. This article includes the 26 item checklist, a separate checklist for the abstract, a template for a CONSORT flowchart for these studies, and an explanation of the changes made and supporting examples. We believe that routine use of this proposed extension to the CONSORT statement will result in improvements in the reporting of pilot trials. Editor's note: In order to encourage its wide dissemination this article is freely accessible on the BMJ and Pilot and Feasibility Studies journal websites.

Methods and processes for development of a CONSORT extension for reporting pilot randomized controlled trials.

BACKGROUND: Feasibility and pilot studies are essential components of planning or preparing for a larger randomized controlled trial (RCT). They are intended to provide useful information about the feasibility of the main RCT-with the goal of reducing uncertainty and thereby increasing the chance of successfully conducting the main RCT. However, research has shown that there are serious inadequacies in the reporting of pilot and feasibility studies. Reasons for this include a lack of explicit publication policies for pilot and feasibility studies in many journals, unclear definitions of what constitutes a pilot or feasibility RCT/study, and a lack of clarity in the objectives and methodological focus. All these suggest that there is an urgent need for new guidelines for reporting pilot and feasibility studies. OBJECTIVES: The aim of this paper is to describe the methods and processes in our development of an extension to the Consolidated Standards of Reporting Trials (CONSORT) Statement for reporting pilot and feasibility RCTs, that are executed in preparation for a future, more definitive RCT. METHODS/DESIGN: There were five overlapping parts to the project: (i) the project launch-which involved establishing a working group and conducting a review of the literature; (ii) stakeholder engagement-which entailed consultation with the CONSORT group, journal editors and publishers, the clinical trials community, and funders; (iii) a Delphi process-used to assess the agreement of experts on initial definitions and to generate a reporting checklist for pilot RCTs, based on the 2010 CONSORT statement extension applicable to reporting pilot studies; (iv) a consensus meeting-to discuss, add, remove, or modify checklist items, with input from experts in the field; and (v) write-up and implementation-which included a guideline document which gives an explanation and elaboration (E&E) and which will provide advice for each item, together with examples of good reporting practice. This final part also included a plan for dissemination and publication of the guideline. CONCLUSIONS: We anticipate that implementation of our guideline will improve the reporting completeness, transparency, and quality of pilot RCTs, and hence benefit several constituencies, including authors of journal manuscripts, funding agencies, educators, researchers, and end-users.

Increased risk of type I errors in cluster randomised trials with small or medium numbers of clusters: a review, reanalysis, and simulation study.

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.