Abstract: A method for simulating a clinical trial includes: selecting a trial procedure for a simulated trial corresponding to the clinical trial; generating a population of subjects for the simulated trial; searching the population of subjects to determine acceptable subjects for the simulated trial; selecting subjects for the simulated trial from the acceptable subjects; simulating the trial procedure for the selected subjects; and collecting trial data for the simulated trial from the simulated trial procedure.

Abstract: A method for generating a continuous mathematical model of a feature common to subjects in a subject group includes selecting a sample data set from each subject in the subject group, selecting a set of expansion functions to be used in the representation of the sample data set, mathematically expanding each member of the sample data set in the form of a summation of results of multiplying each the expansion function in the set of expansion functions by a different mathematical parameter wherein the expanding determines a value for each of the different mathematical parameters, deriving a corresponding distribution function for each of the mathematical parameters, and generating the continuous mathematical model of the feature from the derived distribution functions and the expansion functions. In this way, the model is continuous in time, incorporates dependencies between various parameters, and allows for creation of simulated subjects having pertinent features occurring in real subjects.

Abstract: A method for generating a continuous mathematical model of a feature common to subjects in a subject group includes selecting a sample data set from each subject in the subject group, selecting a set of expansion functions to be used in the representation of the sample data set, mathematically expanding each member of the sample data set in the form of a summation of results of multiplying each the expansion function in the set of expansion functions by a different mathematical parameter wherein the expanding determines a value for each of the different mathematical parameters, deriving a corresponding distribution function for each of the mathematical parameters, and generating the continuous mathematical model of the feature from the derived distribution functions and the expansion functions. In this way, the model is continuous in time, incorporates dependencies between various parameters, and allows for creation of simulated subjects having pertinent features occurring in real subjects.

Abstract: A mathematical model specifically for diabetes may be generated which may be continuous in time, in that there are no discrete time steps, and any event can occur at any times. The model may be generated using differential equations, object oriented programming, and features. The model may be used to simulate patients who have contracted or may contract type 1 or type 2 diabetes, which greatly improves the efficiency of treating patients and designing clinical trials.