1. A computer-implemented method of producing a model for use in predicting time to occurrence of a health-related condition, the method comprising: performing with a data regression and analysis device: obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the health-related condition with respect to the corresponding test subject; and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the health-related condition based upon at least some of the information contained in the vectors of information; wherein for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data; wherein performing the regression comprises using penalty functions that include linear functions of a difference between a predicted value of the model and a target value for the predicted value, and wherein a first slope of the linear function for positive differences between the predicted and target values for the censored-data penalty function is lower than a second slope of the linear function for positive differences between the predicted and target values for the non-censored-data penalty function.