Discussion
In this study, we employed a Bayesian framework that provides a flexible and informative way to handle ICC uncertainty and uses previously published or external ICC estimates. We have presented an extension to the approach proposed by Turner et al.The method suggested by Turner et al.With the conventional approach, when an ICC estimate is imprecise or unreliable, researchers tend to choose a conservative ICC for their sample size calculation.Using complex Bayesian models may require a greater investment of time and expertise. However, the benefit of using a Bayesian model to estimate the trial ICC is that it offers greater flexibility for combining available ICC estimates while incorporating uncertainty and information about the relevance of these estimates into the model.As practical guidance, researchers wishing to explore the utility of the proposed approach would need to consider the following steps:Identify and select ICCs for relevant outcome measures from previous relevant studies, through systematic review and exploring existing databases. The recommendation is to be highly inclusive in terms of potential relevance.Summarise the selected studies with existing ICCs in a Summary Table similar to Identify and invite experts in the topic relevant to the project, agree on elicitation technique, conduct training and task them with a ranking exercise to assign weights As an optional step, we recommend considering evaluation of the expert elicitation and agreement, and differentiate expert input into the model by introducing importance weights Aggregate the elicited weights and embed them into the Bayesian modelling of the targeted ICC.Examine the sensitivity of the conclusions to the chosen model.Choose an appropriate ICC estimate using the Bayesian posterior distribution of the ICC, and then use that to provide one or more estimates of the sample size.The main strength of this study is that we have proposed a practical method of implementing the synthesis of externally available ICCs within a Bayesian framework using expert opinion.The limitation of our approach is the validity and consistency of the reviewers’ ratings. This can be mitigated by increasing the number of reviewers, conducting more targeted training prior to the ranking exercise, and applying different knowledge elicitation techniques. As an extension, strategies for evaluating the elicitation exercise (including ranking reviewers, agreement and coherence checks, calibration)Evaluating the elicited beliefs can be affected by a range of biases.The methodology described in this article is proposed in the context of continuous outcome data, as was Swiger’s approach. However, Swiger’s formula can easily be extended to construct interval estimates for the ICC in the setting of binary outcome clustering by replacing the appropriate quantities in the formula with the binary outcome equivalents,The proposed methodology reduces the impact of uncertainty in the ICC on the design. A next step towards more robust study design could be calculating a mean power (assurance)There are a range of knowledge elicitation tools and techniques which can influence effectiveness of the elicitation. Using online graphical elicitation tool can provide an accessible and intuitive framework for eliciting the information and would naturally produce prior distributions for statistical models.