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Increased popularity of stepped-wedge cluster randomised trials (SW-CRT) highlights the importance of understanding and appropriate mitigation of sources of bias within this trial design. While current evidence suggests that ‘conventional’ cluster randomised controlled trials (RCTs) are at a higher risk of recruitment bias than individually randomised trials, this review aims to estimate the risk of recruitment bias in SW-CRTs.
Systematic review with search conducted on four databases. Risk of bias (RoB) was assessed using subdomain 1a (randomisation process) and 1b (timing of identification or recruitment of participants) of the Cochrane RoB tool 2.0 (extension for cluster RCTs).
MEDLINE, Embase, CINAHL, Cochrane Library were searched on 9 February 2024.
SW-CRTs published in 2023 were included.
Two independent reviewers screened and extracted all eligible papers. RoB was assessed with the Cochrane RoB tool.
Overall, 808 papers were screened, and 64 studies were included in the review. Most studies were deemed to have a high RoB (n=35, 55%), some concerns were noticed in 20 studies (31%), and 9 (14%) were considered to have a low RoB. The description of the randomisation process in the included papers was sometimes poorly reported (in 15 studies (23%) problems with the randomisation process were identified), and 21 studies (33%) had issues with sampling strategy (recruiting participants after randomisation by unmasked staff).
The review revealed that SW-CRTs are prone to recruitment bias, but the risks are comparable to cluster RCTs. When SW-CRTs are unable to recruit prior to randomisation, mitigation strategies could be implemented to reduce bias. A separate tool for RoB assessment in SW-CRTs is required to address the complexities of this trial design.
by Megan Wiggins, Marie Varughese, Ellen Rafferty, Sasha van Katwyk, Christopher McCabe, Jeff Round, Erin Kirwin
BackgroundDuring public health crises such as the COVID-19 pandemic, decision-makers relied on infectious disease models to evaluate policy options. Often, there is a high degree of uncertainty in the evidence base underpinning these models. When there is increased uncertainty, the risk of selecting a policy option that does not align with the intended policy objective also increases; we term this decision risk. Even when models adequately capture uncertainty, the tools used to communicate their outcomes, underlying uncertainty, and associated decision risk have often been insufficient. Our aim is to support infectious disease modellers and decision-makers in interpreting and communicating decision risk when evaluating multiple policy options.
MethodsWe developed the Decision Uncertainty Toolkit by adapting methods from health economics and infectious disease modelling to improve the interpretation and communication of uncertainty. Specifically, we developed a quantitative measure of decision risk as well as a suite of risk visualizations. We refined the toolkit contents based on feedback from early dissemination through conferences and workshops.
ResultsThe Decision Uncertainty Toolkit: (i) adapts and extends existing health economics methods for characterization, estimation, and communication of uncertainty to infectious disease modelling, (ii) introduces a novel risk measure that quantitatively captures the downside risk of policy alternatives, (iii) provides visual outputs for dissemination and communication of uncertainty and decision risk, and (iv) includes instructions on how to use the toolkit, standard text descriptions and examples for each component. The use of the toolkit is demonstrated through a hypothetical example.
ConclusionThe Decision Uncertainty Toolkit improves existing methods for communicating infectious disease model results by providing additional information regarding uncertainty and decision risk associated with policy alternatives. This empowers decision-makers to consider and evaluate decision risk more effectively when making policy decisions. Improved understanding of decision risk can improve outcomes in future public health crises.
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