title: GOLDmineR: improving models for classifying patients with chest pain.
abstract: The laboratory is dealing with reporting tests as information needed to make clinical decisions. The traditional statistical quality control measures which assigns reference ranges based on 95 percent confidence intervals is insufficient for diagnostic tests that assign risk. We construct a basis for risk assignment by a method that builds on the 2 x 2 contingency table used to calculate the C2 goodness-of-fit and Bayesian estimates. The widely used logistic regression is a subset of the regression method, as it only considers dichotomous outcome choices. We use examples of multivalued predictor(s) and a multivalued as well as dichotomous outcome. Outcomes analyses are quite easy using the ordinal logit regression model.
authors.affiliation: Department of Pathology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, Connecticut, USA.
authors.forename: Larry
authors.initials: L
authors.lastname: Bernstein
authors.forename: Keith
authors.initials: K
authors.lastname: Bradley
authors.forename: Stuart
authors.initials: S
authors.lastname: Zarich
mesh_terms: D000465:Algorithms; D002637:Chest Pain / Q000175:diagnosis*; D003937:Diagnosis, Differential; D004562:Electrocardiography; D006801:Humans; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000175:diagnosis*; D016017:Odds Ratio; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012306:Risk; D020107:Troponin T / Q000032:analysis
chemical_list: D020107:Troponin T