Abstract: A method of assessing risk of an individual for a disease is disclosed. A level of a marker for the disease is detected, and the risk of disease is computed from a statistical analysis of the marker level distributions for tested normal and diseased populations, using multivariate distributions. The computed risk is compared to thresholds to triage the individual into a normal, borderline, or elevated risk group, and a course of action based on the risk group is determined. Memory storing a program for execution on a data processing system, as well as a computer system comprising said memory is also disclosed, the memory comprising a receiver for signals representative of levels of a marker for the disease; a central processing unit for computing risk of disease from one or more levels of the marker, and for comparing the computed risk to thresholds to triage the individual; and storage medium for the marker levels for subsequent computations of risk of disease.

Abstract: A method of assessing risk of an individual for a disease is disclosed. A level of a marker for the disease is detected, and the risk of disease is computed from a statistical analysis of the marker level distributions for tested normal and diseased populations, using multivariate distributions. The computed risk is compared to thresholds to triage the individual into a normal, borderline, or elevated risk group, and a course of action based on the risk group is determined. Memory storing a program for execution on a data processing system, as well as a computer system comprising said memory is also disclosed, the memory comprising a receiver for signals representative of levels of a marker for the disease; a central procesing unit for computing risk of disease from one or more levels of the marker, and for comparing the computed risk to thresholds to triage the individual; and storage medium for the marker levels for subsequent computations of risk of disease.

Abstract: A method for improved measurement of a concentration of an analyte in a sample having an unknown concentration of the analyte includes using an affinity assay having an experimental indicator. Standard and control samples, each having a known concentration of the analyte are assayed and a response, an amount of experimental indicator emitted for each sample is detected. The unknown sample is also assayed and its response is detected. A prior probability density is supplied for the unknown concentration, along with a model for a standard curve relating an expected response to a concentration of the analyte. A posterior density, having a median, for the unknown concentration is generated based on the supplied prior density, the supplied model, and the responses for the standard samples, the control samples and the unknown samples, by applying Bayes' rule.