The study reports a mathematical method for deciding which clinical data are of importance for treatment decisions in a given clinical setting. The method comprises the following steps: (A) the receiver operator characteristic (ROC) functions of the compared sets of data are computed; (B) the design and aim of the clinical study is expressed as an integral measure on the space of sensitivity values (this reflects the preference of low or high sensitivity values dependent on the clinical targets); (C) the sets of data being compared are characterised by the non-linear integrals of their ROC functions. The approach has been used to compare mean heart rate (HR) and heart rate variability (HRV) data calculated in 5113 different portions of 24-h ECG recordings and assessed in 365 patients surviving acute myocardial infarction, in order to evaluate the utility of Holter recording of varying lengths and starting times for the prediction of sudden cardiac death and/or serious arrhythmic events. The results of the study show that this approach is capable of evaluating and comparing the sets of medical data used for identification of patients who are at increased risk. The experimental part of the study showed that the optimum recording interval for the assessment of HR and HRV data in patients who survived acute myocardial infarction depends on the aim of the identification of increased risk patients. The optimum interval of recording is different for an identification which requires a low number of false negative cases and permits a higher number of false positive cases, than for the situation where a low number of false positive cases are required and a higher number of false negative cases are permissible.