title: The asymptotic efficiency of tests using misclassified data in contingency tables.
abstract: A simple expression is given for the approximate upper bound of the asymptotic relative efficiency of tests between nested loglinear models using misclassified data versus those using data with no classification errors. This efficiency depends on the probabilities of data being misclassified into the wrong cells of the contingency table. An example demonstrates that the loss of efficiency due to misclassification can be substantial.
mesh_terms: D001172:Arthritis, Rheumatoid / Q000175:diagnosis*; D003951:Diagnostic Errors; D005260:Female; D006801:Humans; D008297:Male; D008433:Mathematics; D008954:Models, Biological; D010146:Pain; D010808:Physical Examination; D012216:Rheumatic Diseases / Q000175:diagnosis*; D012737:Sex Factors