What is it about?
We discuss the application of cluster-randomized design of intervention studies to simulation-based evaluation of surgical care policies. We introduce a framework and study design to evaluate methods for improving the surgical care process with the use of patient flow models that simulate the steps in service delivery and response pathways for individual patients.
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Why is it important?
Patient-level outcomes in a given simulation run may be correlated, therefore we suggest a cluster-randomized design of experiment for determining how many simulation runs are required and how input factors should vary across the runs. In such a design, simulation runs rather than simulated individuals are randomized to different study groups. For patient outcomes that vary more across simulation runs than within each run, we provide formulas to be adapted in sample size calculations to allow for clustering of responses.
Perspectives
Healthcare intervention studies constitute a common tool for comparing existing and proposed alternatives in management and policy. We submit the methodological rigour of evaluative studies should be applied to the design and analysis of simulation experiments.
Professor Boris Sobolev
University of British Columbia
Read the Original
This page is a summary of: Cluster-randomized design for simulation-based evaluation of complex healthcare interventions, Journal of Simulation, March 2010, Nature,
DOI: 10.1057/jos.2009.14.
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