Sample size determination for stepped wedge cluster randomized trials in pragmatic settings

The stepped-wedge cluster randomized design has been increasingly employed by pragmatic trials in health services research. In this study, based on the GEE approach, we present closed-form sample size calculation that is applicable to both closed-cohort and cross-sectional stepped wedge trials. Importantly, the proposed method is flexible to accommodate design issues routinely encountered in pragmatic trials, such as different within- and between-subject correlation structures, irregular crossover schedules for the switch to intervention, and missing data due to repeated measurements over prolonged follow-up. The closed-form formulas allow researchers to analytically assess the impact of different design factors on sample size requirement. We also recognize the potential issue of limited numbers of clusters in pragmatic stepped wedge trials and present an adjustment approach for underestimated variance of the treatment effect. We conduct extensive simulation to assess the performance of the proposed sample size method. An application example to a real clinical trial is presented.

The hunt for efficient, incomplete designs for stepped wedge trials with continuous recruitment and continuous outcome measures

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.

Design, rationale and analysis plan for the Stand Up for Health trial in contact centres: a stepped wedge feasibility study

Background Contact centres are one of the most sedentary workplaces, with employees spending a very high proportion of their working day sitting down. About a quarter of contact centre staff regularly experience musculoskeletal health problems due to high levels of sedentary behaviour, including lower back pain. There have been no previous randomised studies specifically aiming to reduce sedentary behaviour in contact centre staff. To address this gap, the Stand Up for Health (SUH) study aims to test the feasibility and acceptability of a complex theory-based intervention to reduce sedentary behaviour in contact centres. Methods The Stand Up for Health study has a stepped wedge cluster randomised trial design, which is a pragmatic design whereby clusters (contact centres) are randomised to time points at which they will begin to receive the intervention. All contact centre staff have the opportunity to experience the intervention. To minimise the resource burden in this feasibility study, data collection is not continuous, but undertaken on a selective number of occasions, so the stepped wedge design is “incomplete”. Eleven contact centres in England and Scotland have been recruited, and the sample size is approximately 27 per centre (270 in total). The statistical analysis will predominantly focus on assessing feasibility, including the calculation of recruitment rates and rates of attrition. Exploratory analysis will be performed to compare objectively measured sedentary time in the workplace (measured using an activPAL™ device) between intervention and control conditions using a linear mixed effects regression model. Discussion To our knowledge, this is the first stepped wedge feasibility study conducted in call centres. The rationale and justification of our novel staircase stepped wedge design has been presented, and we hope that by presenting our study design and statistical analysis plan, it will contribute to the literature on stepped wedge trials, and in particular feasibility stepped wedge trials. The findings of the study will also help inform whether this is a suitable design for other settings where data collection is challenging. Trial registration The trial has been registered on the ISRCTN database: http://www.isrctn.com/ISRCTN11580369

Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks

Randomized controlled trials are crucial for the evaluation of interventions such as vaccinations, but the design and analysis of these studies during infectious disease outbreaks is complicated by statistical, ethical, and logistical factors. Attempts to resolve these complexities have led to the proposal of a variety of trial designs, including individual randomization and several types of cluster randomization designs: parallel-arm, ring vaccination, and stepped wedge designs. Because of the strong time trends present in infectious disease incidence, however, methods generally used to analyze stepped wedge trials might not perform well in these settings. Using simulated outbreaks, we evaluated various designs and analysis methods, including recently proposed methods for analyzing stepped wedge trials, to determine the statistical properties of these methods. While new methods for analyzing stepped wedge trials can provide some improvement over previous methods, we find that they still lag behind parallel-arm cluster-randomized trials and individually randomized trials in achieving adequate power to detect intervention effects. We also find that these methods are highly sensitive to the weighting of effect estimates across time periods. Despite the value of new methods, stepped wedge trials still have statistical disadvantages compared with other trial designs in epidemic settings.