Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice

Patient outcomes depend crucially on the treatment provider delivering the intervention. Where there is more than one treatment provider, outcomes observed in patients treated by the same treatment provider may be more similar than those in patients treated by other treatment providers, a phenomenon known as clustering. Whilst treatment providers are often thought of as health professionals, such as general practitioners, nurses, surgeons or therapists, the potential for clustering is also present for treating centre within a clinical trial [1, 2]. In addition to clustering, a change in skill in treatment delivery may be observed over time; specifically, there may be a learning element experienced within one or all of the arms of the study observed during the course of the trial, meaning that trial outcomes may also change and be associated with changes in skill [3]. When comparing interventions within a clinical trial, it is imperative that any trial is designed under a common protocol with regard to treatment delivery, and that the trial is conducted in accordance with this protocol. At trial outset, a researcher may consider the homogeneity of any intervention under examination and the degree to which it is appropriate to standardise these procedures [4]. In extreme cases, where the trial results are questioned by the research community related to the study results, the trial team should be prepared to alleviate any doubts of heterogeneity of treatment effects [5].

Difference in treatment delivery is often considered more of a concern in trials investigating a complex intervention, such as surgery. Trials involving a complex intervention are often criticised because of variability between intervention providers (clustering) but also due to variability over time, often as a result of increased experience (learning) [4]. Recognition, and management as appropriate, of clustering and learning is recommended, and it may have increased relevance within the surgical field, dependent upon the interventions being investigated and their routine use [2, 4, 6‐9]. Considering these aspects at trial outset will ensure that any necessary adjustment, to the design or analysis of the study, is applied in a manner appropriate for the intervention under investigation and will support clinical decision-making [5].

Whilst the notion of clustering and learning is familiar to many statisticians, the extent to which these considerations are made, and how, is unknown. A survey to establish current practice for the statistical management of clustering and learning effects in the design and analysis of randomised multicentre trials was undertaken within UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Units (CTUs) [10]. This survey aimed to ascertain the UK-wide experience of running multicentre studies, in particular those investigating a complex or surgical intervention, in addition to establishing awareness of design issues associated with these studies and levels of concerns around these issues.

The survey was delivered at the bi-annual Statisticians Operational Group Meeting in April 2018. Attendees were statistical representatives from each of the UKCRC Registered CTUs [10]. Units that did not have a representative present at this meeting, or did not respond, were contacted via email following the event and invited to participate. Registered CTUs were identified from the network website [10] on 4 January 2018 (n = 51, of which 50 were registered at the time of survey; see Supplementary Box 1). As the survey involved professionals and discussions of current practice, no formal ethical approvals were deemed necessary.

This survey identifies the fact that, despite multicentre trials being prominent across all CTUs, there is a UK-wide variation of designing and analysing these trials with respect to clustering and learning effects. Approximately half of Units changed their approach to design and analysis when presented with five example trial scenarios, each with varying levels of complexity, such as small sample size per centre and complex interventions, such as surgery. This finding suggests that variation can exist both across and within Units, suggesting that this decision can depend on the type of trial being conducted. Units indicate awareness of the potential methodological challenges associated with the design and analysis of multicentre trials, although the approaches used and opinions on these vary. The high response rate achieved provides insight into the general and current practice of managing clustering and learning effects in multicentre trials investigating varying types of interventions. Whilst acknowledging that different approaches may be more suitable to different trial types, they indicate the need for a more unified approach to the design and analysis of trials where outcomes are associated with the delivery of the intervention and/or more research in this field.

When adjusting for clustering within the design, a higher proportion than expected ran trials that did not stratify by centre (52%). Most commonly, this was due to too many centres and not enough participants within centre. Stratifying by centre was most common in all scenarios, whilst stratifying by treatment provider was consistently rare but more common in trials with a surgical intervention. Stratifying by treatment provider raised pragmatic concerns, e.g. concerns over relevance to research question, or provider not known pre-randomisation. Whilst in some settings, such as emergency treatment, advance knowledge of the treatment provider will be unobtainable, advanced planning may be possible in other settings, such as group therapy, with guidance for practical issues like these available [13]. Half of the responders had adjusted by centre following stratification by the same. Most commonly this was done by pre-specified grouping rules established at the design stage or using an ad hoc approach determined after design due to small numbers per group. Regardless of stratification approach, eight-tenths of responders had adjusted for centre in the statistical model. There were mixed opinions on how this adjustment was made, i.e. by fixed or random effects with reasons provided for and against both approaches. When a positive treatment effect is found, three-quarters and one-third stated that they then explore heterogeneity by centre and treatment provider respectively. All did so using graphical displays.

Managing learning by design through defining a minimum level of expertise for health professionals participating in the trial [4] was most common, with almost all responders (89%) applying this approach to studies within their Unit. Less than a third indicated experience in conducting expertise-based designs, a design that can be particularly useful when comparing substantially different interventions. This finding suggests these designs are more commonly implemented than suggested by the literature [7, 14]. Concerns were raised that identifying evidence of learning may lead to ’awkward additional data summaries’.

Guidance on trial design and analysis does exist, with the most relevant of these recommendations being explicitly incorporated into the survey questions [4, 6] Supplementary Box 2. Additional documents within the International Council for Harmonisation (ICH) Series provide further guidance beyond ICH E9 [15, 16]. The Consolidated Standards of Reporting Trials (CONSORT) statement and relevant extensions provide direction valuable at study design despite the document being developed to support reporting [17, 18]. The decision to explore effects may, in part, be related to the intention of the research in terms of how the results will be used, and the PRECIS-2 tool has been developed to help with this [19]. However, the ability to identify and explore heterogeneity at the analysis stage is an important consideration for generalisability for all trials.

Strengths of this study were that, although the survey was limited to registered Units, responders represent wide geographic coverage within the UK, spanning a diverse range of medical conditions and associated methodologies. In addition, participating Units are known to comply with required regulatory standards and meet acceptable standards of quality required by the UKCRC CTU registration process [20]. All responders were experienced triallists who either were statistical lead at their Unit or a nominated statistical representative. Publicly funded trials cover a diversity of interventions [15] and are generally not seeking a marketing authorisation from the competent authorities, and this may impact the approaches taken in line with heterogeneity of effects by cluster or time. Limitations of this work are that it represents statistical practice within the UK in leading trial centres, with global practice unknown. However, the survey drew upon internationally accepted guidelines [4] for best practice, and therefore the opinions and experiences are applicable beyond the UK. Second, some of the observed responses may have related to the different types of trials that the CTUs conduct. Not all trials include interventions where there is learning. Indeed, one would anticipate that many pragmatic large-scale trials do not have ‘learning’ effects because they include interventions that are stabilised and in widespread use. Whilst the survey allowed for free text responses, a more focussed survey, achieved using qualitative research methods, would be needed to examine these issues. Third, the volume of studies designed by each Unit will vary widely, and one responder per Unit may result in experiences reported for larger Units not being indicative of all studies run. However, responders were able to complete the survey with additional support within their Unit if deemed appropriate.

This survey is the first to report on the experience and management approaches with regard to clustering effects and the learning curve in multicentre randomised trials. Importantly, responders, who were highly experienced in the design and analysis of such studies, appear to have awareness of when to make such considerations. Whilst approaches to management are varied, and this variation may be trial-dependent within Unit, reasons for approaches reported were provided and approaches justified. Historically, guidance on the design, analysis and reporting of randomised controlled trials was developed more generally to support consistency in approaches across a more conventional randomised controlled trial [4, 15, 16], with the development of more intervention-specific guidelines being established following these to address the additional complexities across different types of trials [11, 17, 18, 21]. Intervention-specific guidelines may have led to the variation and justifications identified in this survey. Results highlight the need for better consistency between triallists. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered and/or further researched to establish optimal methods.