Medical Research

Nationwide Children's Hospital

Veronica Vieland, Jayajit Das, Susan Hodge, Sang-Cheol Seok
Columbus, OH
December 2014

Biomedical research relies on the statistical assessment of the strength of the evidence for or against hypotheses based on scientific data.  A variety of measures are used as evidence measures such as p-values.  But our standard measures of evidence lack calibration: A given change in any particular measure does not always correspond to the same amount of change in the strength of evidence; different measures are on fundamentally different scales; and any one of them may indicate decreasing strength of evidence while the evidence strength is actually increasing.  Just as failure to properly calibrate experimental equipment can lead to scientific errors, uncalibrated evidence measures can lead to erroneous interpretations of biological data.  The goal of this proposal is develop an absolute (context-independent) measure of the strength of evidence, through a novel information-dynamic paradigm (IDP).  Combining theory development with high-performance computing we will extend the current prototype IDP to general statistical models and apply it to two distinct biomedical fields, human statistical genetics and biophysical modeling of the immune system.

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