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The authors aimed to use existing data to create a microsimulation model of the primary care process in New Zealand, including realistically simulating the allocation of general practitioners (GPs) to a population sample. This is important because GP behavior is likely to be a major determinant of future cost and service outcomes. Two nationally representative data sets were matched: a sample of GPs and their patients from the National Primary Medical Care Survey (NPMCS) and a population sample from the New Zealand Health Survey (NZHS). Matching involved first dividing the data sets into cells based on common variables. Further variables were then included in a distance function to guide matching within cells. A transportation optimization algorithm allocated GPs based on these—on similarities in patients’ attributes. Statistical matching performed well with high correlations for patient attributes and reduced …