Reliability of Bayesian probability analysis for predicting coronary artery disease in a veterans hospital

To assess the accuracy of Bayesian probability analysis for the prediction of coronary artery disease, post-test probabilities were generated by the application of three Bayesian algorithms to the clinical and noninvasive test results of 199 patients undergoing angiography in a veterans' hospital. All assumed conditional independence but each used different pre-test and conditional probabilities. Two statistical approaches were employed: (1) Sorting of patients in ascending deciles of probability and comparing expected and observed probabilities in each decile. (2) Calculation of normally distributed reliability statistics which do not depend on probability subsets and the comparison of resulting probability distributions using these statistics. Both statistical approaches revealed that the Bayesian algorithms overestimated disease probability when it was high and underestimated it when low. Though all three algorithms were frequently incorrect, they differed significantly in their accuracies, suggesting that errors in Bayesian analysis are caused by factors other than the assumption of independence. The errors may be due to differences in sensitivity and specificity of tests applied in different institutions.     

The reliability of probability analysis in the prediction of coronary artery disease in two hospitals

To assess the accuracy of the Bayesian computer program CADENZA for the prediction of coronary artery disease, the authors examined the probabilities generated by the application of this program to the clinical and noninvasive test results of 303 patients in a private referral center and 199 patients in a veterans' hospital. These probabilities were compared with those produced by applying a six-variable discriminant function derived by logistic regression at the private referral center. Two statistical approaches were employed in evaluating the relative performances of the Bayesian program and the discriminant function. The first of these involved the sorting of patients in both test groups into ascending deciles of probability and comparing expected probability with observed angiographic disease prevalence in each decile. The second involved the calculation and comparison of a standardized reliability measure. The latter was significantly lower for the discriminant function both at the private hospital (0.200 for the discriminant function versus -17.5 +/- 1.96 for the Bayesian program) and at the veterans' hospital (-0.8 +/- 1.96 for the discriminant function versus -11.3 for Bayesian program). This suggests that the discriminant function is significantly superior to the Bayesian algorithm CADENZA for predicting coronary artery disease probabilities in subjects who have relatively high pretest disease probabilities.     

Quantitative study of the immunoglobulin-containing cells in trephine samples of bone marrow

Immunoglobulin (Ig) was demonstrated in paraffin sections of 12 trephine bone marrow biopsies by means of the unlabelled antibody peroxidase-antiperoxidase (PAP) method. The Ig-containing cells, which were counted with the Reichert-Jung (Kontron) MOP-AMO₃ user-controlled image-analyser, were found to constitute approximately 4·2% of all the nucleated cells in the marrow, a figure significantly higher than those reported by previous workers.