Circulation, Vol 73, 970-977, Copyright © 1986 by American Heart Association
R Detrano, J Leatherman, EE Salcedo, J Yiannikas and G Williams
Both Bayesian analysis assuming independence and discriminant function
analysis have been used to estimate probabilities of coronary disease. To
compare their relative accuracy, we submitted 303 subjects referred for
coronary angiography to stress electrocardiography, thallium scintigraphy,
and cine fluoroscopy. Severe angiographic disease was defined as at least
one greater than 50% occlusion of a major vessel. Four calculations were
done: (1) Bayesian analysis using literature estimates of pretest
probabilities, sensitivities, and specificities was applied to the clinical
and test data of a randomly selected subgroup (group I, 151 patients) to
calculate posttest probabilities. (2) Bayesian analysis using literature
estimates of pretest probabilities (but with sensitivities and
specificities derived from the remaining 152 subjects [group II]) was
applied to group I data to estimate posttest probabilities. (3) A
discriminant function with logistic regression coefficients derived from
the clinical and test variables of group II was used to calculate posttest
probabilities of group I. (4) A discriminant function derived with the use
of test results from group II and pretest probabilities from the literature
was used to calculate posttest probabilities of group I. Receiver operating
characteristic curve analysis showed that all four calculations could
equivalently rank the disease probabilities for our patients. A
goodness-of-fit analysis suggested the following relationship between the
accuracies of the four calculations: (1) less than (2) approximately equal
to (4) less than (3). Our results suggest that data- based discriminant
functions are more accurate than literature-based Bayesian analysis
assuming independence in predicting severe coronary disease based on
clinical and noninvasive test results.
ARTICLES
Bayesian analysis versus discriminant function analysis: their relative utility in the diagnosis of coronary disease
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