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Results for “Bayesian analysis”

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At least 163 records · Page 9Linked to original sources
PubMed1993

Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory.

Only two radiographic findings allow reliable distinction of benign from malignant solitary pulmonary nodules. Intuitively, it is clear that other radiographic and clinical findings should also be important in making this distinction. Subjectively incorporating these other findings into the decision of whether a nodule is benign or malignant is difficult. Likelihood ratios, which indicate the degree of malignancy or benignity represented by a test result or clinical finding, can be combined by means of the Bayes theorem to quantitate the probability of malignancy of a given nodule. From a literature survey, likelihood ratios were derived for six radiographic and four clinical characteristics associated with solitary pulmonary nodules. There were a total of 15 malignant and 19 benign findings, the most important of which were radiographic characteristics. For malignant nodules, the most important radiographic characteristics were thickness of the cavity wall spicular edge, and diameter of over 3 cm. For benign nodules, the most important radiographic characteristics were benign growth rate and a benign pattern of calcification.

Adult↗
PubMedDate not supplied

Bayesian analysis of diastolic blood pressure measurement.

A mathematical model is presented for measurements that include substantial fluctuation and error. Under the assumptions that the fluctuation-error variance is the same for all subjects, and that the distributions of fluctuation-error variance within subjects and "true" values of the measurements in the population are normal, Bayes' theorem produces a simple estimate of the "true" value of a measurement, and a standard error, conditional on a single observation. The model is easily extended to several observations. Methods for estimating the parameters of the model from a data set are presented, and applied to diastolic blood pressures of patients in the authors' primary care clinic. The test-retest reliability of a single blood pressure measurement for this population is 0.41. Because continuous measurements are often dichotomized into "normal" and "abnormal" ranges by a threshold criterion, the authors present formulas for the positive predictive value when a decision rule based on a given number of observations is used in a population with respect to a threshold criterion for the "true" values. For example, classifying their patients as hypertensive on the basis of the average of two readings exceeding 90 mm Hg diastolic pressure would have a positive predictive value of 52% for the "gold standard" of average diastolic pressure exceeding 90 mm Hg. Formulas to calculate the frequency with which patients will be classified "abnormal" by one decision rule but will be classified "normal" by later application of another rule are provided and used to "predict" the frequency with which this crossover phenomenon should have occurred in the enrollment phase of the Hypertension Detection and Follow-up Programs.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMedDate not supplied

The single-cutoff trap: implications for Bayesian analysis of stress electrocardiograms.

Quantitative analysis of exercise electrocardiograms has been emphasized by many investigators. Specific problems have been found when a single cutoff is used to define a positive or a negative test: a single cutoff does not distinguish stress electrocardiography results that are slightly positive from those that are markedly positive. This may lead clinicians to underweigh strong evidence for or against coronary artery disease. This study evaluated clinicians' quantitative analysis of stress electrocardiograms. Two hundred and thirty-five physicians interpreted the results of mildly positive (1.2 mm ST-segment depression) and strongly positive (2.2 mm ST-segment depression) stress electrocardiograms. Their posttest probability estimates were too high for a mildly positive test (0.62 +/- 0.02 versus actual of 0.38; p less than 0.001) and too low for a strongly positive test (0.77 +/- 0.01 versus actual of 0.98; p less than 0.001). Physicians should understand decision aids and should use multiple rather than single cutoffs to interpret the results of stress electrocardiography.

Bayes Theorem↗
PubMedDate not supplied

A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits.

Gompertz growth curves were fitted to the data of 137 rabbits from control (C) and selected (S) lines. The animals came from a synthetic rabbit line selected for an increased growth rate. The embryos from generations 3 and 4 were frozen and thawed to be contemporary of rabbits born in generation 10. Group C was the offspring of generations 3 and 4, and group S was the contemporary offspring of generation 10. The animals were weighed individually twice a week during the first four weeks of life, and once a week thereafter, until 20 weeks of age. Subsequently, the males were weighed weekly until 40 weeks of age. The random samples of the posterior distributions of the growth curve parameters were drawn by using Markov Chain Monte Carlo (MCMC) methods. As a consequence of selection, the selected animals were heavier than the C animals throughout the entire growth curve. Adult body weight, estimated as a parameter of the Gompertz curve, was 7% higher in the selected line. The other parameters of the Gompertz curve were scarcely affected by selection. When selected and control growth curves are represented in a metabolic scale, all differences disappear.

Animals↗
PubMed1986

Diagnosing liver metastases: a Bayesian analysis.

Clinicians frequently perform tests to determine whether patients have liver metastases. Optimal use of a laboratory test requires that the clinician know the test's operating characteristics (its sensitivity and specificity) and have an estimate of the pretest probability that disease is present. We have surveyed studies that examined the value of four biochemical and three imaging tests in establishing a diagnosis of hepatic metastases in patients who underwent an invasive procedure to establish the presence or absence of disease. We have pooled the data from these studies to arrive at values for the sensitivity and specificity of each of these tests, and calculated the predictive values for these tests over a wide range of pretest probabilities of disease. Several examples illustrate how this information may be used clinically. We provide a framework for the optimal interpretation of these commonly ordered tests and indicate the data needed for their complete analysis.

Bayes Theorem↗
PubMed2001

Bayesian analysis of liability of clinical mastitis in Norwegian cattle with a threshold model: effects of data sampling method and model specification.

First-lactation records of Norwegian Cattle were used to infer heritability of liability to clinical mastitis with a threshold sire model. Mastitis was defined as a binary response (presence or absence) in a defined period of first lactation (opportunity period). Length of opportunity period (from 30 d before calving up to 120 or 300 d of lactation) had less effect on heritability estimates than data sampling methods (include or exclude records of cows culled before the end of the opportunity period) whereas sire ranking was more affected by the former. Including all cows, whether culled before the end of the opportunity period or not, gave a sharper and more symmetric posterior distribution of heritability of liability to clinical mastitis. When we analyzed data for all cows, model specification had a small effect on heritability estimates, while sire ranking was affected markedly. Posterior means of heritability range from 0.058 to 0.074. A model regressing on the length of the opportunity period for culled cows without mastitis, was shown favorable for the two opportunity periods using Bayes factors and the deviance information criterion for model comparison. This model, in which liability of mastitis depends on time to culling, may allow utilizing information from all first lactations in genetic evaluation, irrespectively of duration and culling outcome.

Animals↗
PubMed1992

[Significance of inflammatory syndrome in the diagnosis of Horton's disease. Attempt at the application of Bayesian analysis].

This study was designed to investigate the value of biologic evidence of inflammation for the diagnosis of giant cell arteritis. Experienced physicians were asked to evaluate five pairs of medical records based on real cases. In each pair, one case lacked biologic evidence of inflammation. This study offered the opportunity to explore the feasibility of a simplified Bayes model. A blind evaluation obtained by showing the paired case-reports with similar evidence of inflammation in both cases of each pair to 14 specialty physicians yielded a likelihood of diagnosis of +/- 20%. Analysis of the 46 responses to the study demonstrated, despite wide variations, a significantly greater likelihood of diagnosis in the cases with evidence of inflammation. Nevertheless, 17% to 36% of physicians--according to the case-report--ascribed virtually no importance to the ESR. Most of the physicians considered temporal artery biopsy was warranted when the likelihood of diagnosis was greater than 25%. Emergency corticosteroid therapy while awaiting the histologic results was approved by most responders when the likelihood of diagnosis was greater than 65%. The "pre-test" likelihood, calculated assuming that sensitivity and specificity of the ESR are 0.99 and 0.50, respectively, ranged from 0.89 to 0.98 for the case-reports with no evidence of inflammation and from 0.16 to 0.59 for the case-reports with evidence of inflammation In theory, the figures for the two types of case-report should not differ by more than 20%. Use of a low value for specificity (0.05) would improve the fit of values in cases without evidence of inflammation but would increase discrepancies in the other cases.(ABSTRACT TRUNCATED AT 250 WORDS)

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