BACKGROUND: In diagnostic evaluation, it is necessary to assess the clinical impact of a new diagnostic as well as its diagnostic accuracy. The comparative interrupted time series design has been proposed as a quasi-experimental approach to evaluating interventions. We show how it can be used in the design of a study to evaluate a point-of-care diagnostic test for C-reactive protein in out-of-hours primary care services, to guide antibiotic prescribing among patients presenting with possible respiratory tract infection. This study consisted of a retrospective phase that used routinely collected monthly antibiotic prescribing data from different study sites, and a prospective phase in which antibiotic prescribing rates were monitored after the C-reactive protein diagnostic was introduced at some of the sites. METHODS: Of 8 study sites, 3 were assigned to receive the diagnostic and 5 were assigned as controls. We obtained retrospective monthly time series of respiratory tract targeted antibiotic prescriptions at each site. Separate ARIMA models at each site were used these to forecast monthly prescription counts that would be expected in the prospective phase, using simulation to obtain a set of 1-year predictions alongside their standard errors. We show how these forecasts can be combined to test for a change in prescription rates after introduction of the diagnostic and estimate power to detect this change. RESULTS: Fitted time series models at each site were stationary and showed second-order annual seasonality, with a clear December peak in prescriptions, although the timing and extent of the peak varied between sites and between years. Mean one-year predictions of antibiotic prescribing rates based on the retrospective time series analysis differed between sites assigned to receive the diagnostic and those assigned to control. Adjusting for the trend in the retrospective time series at each site removed these differences. CONCLUSIONS: Quasi-experimental designs such as comparative interrupted time series can be used in diagnostic evaluation to estimate effect sizes before conducting a full randomised controlled trial or if a randomised trial is infeasible. In multi-site studies, existing retrospective data should be used to adjust for underlying differences between sites to make outcome data from different sites comparable, when possible.