Eliciting information for Bayesian hierarchical ICC modelling
TIA, stroke
SUTTON, STROKE, TIA, TRANSIENT ISCHEMIC ATTACK
Although a single robust ICC estimate was not available for the planned cluster-randomised trial, there were a number of studies available with ICC estimates that had varying degrees of relevance, both to the planned trial population and its primary outcome. To identify a set of ICC estimates relevant to our setting, we conducted a structured literature search. To identify the studies, the trial team utilised the search strategy implemented in a paper by Sutton et al.Characteristics of studies with available relevant ICC estimates included in Bayesian modelling.ICC: intracluster correlation coefficient; TIA: transient ischemic attack; NHS: National Health Service.To combine all 34 ICCs, we used the following model suggested by Turner et al.
here, Similar to Turner et al.,In practice, however, it is unclear how to assign these weights. GuidanceTo minimise subjectivity in defining weights, we considered using a knowledge elicitation process to derive study and outcome weights from expert prior opinion or beliefs. We performed a ranking exercise where eight members of the trial team, with relevant expertise, assigned weights for each trial and for each outcome. The expert team included investigators with the following expertise: health-service and stroke researchers, nurses, methodologists and a stroke-survivor (member of ICONS Patient and Public Involvement group).To aid elicitation, we produced an information leaflet (see The eight experts were tasked with assigning study and outcome weights. The ranking exercise was set up in two stages. The first stage comprised a group exercise and was conducted face-to-face, in line with methodological developments suggesting that elicitation within group meetings is an efficient way of collecting expert opinion.The implemented knowledge elicitation process is a version of the probabilistic Delphi protocol