Clinical trials are the commonly accepted proofs of validity of therapeutic approaches in most medical fields. In many cases, a therapy approach is defined a priori and administered by medical personnel during the trial. In the case of type 1 diabetes mellitus (T1DM), the therapy approach typically consists in fixing a rule for the intake of insulin and taking the corresponding decisions during the day according to measurements and inputs by the patient, e.g. the expected carbohydrate intake. As clinical trials are expensive and complex to realize, only few variants can be really tested. However, in view of the large number of possible options, it would be very useful to be able to test a larger number of variants. Against this background, recently, several methods have been proposed in the scientific literature to extrapolate the effect of a modified therapy using real measurements as baseline. The key idea of all these methods consists in splitting the measurements into a controllable part and a ?disturbance? component, which is assumed to be independent from the control action, i.e. from the insulin delivery. Of course, this splitting depends on the specific model assumptions, and the evaluation results of modified therapies may change according to the specific assumed model. However, as this paper shows at the example of the comparison between a standard and an adaptive bolus calculator, the results seem to become consistent if a large enough, representative dataset is used.