Hepatobiliary scan and acute cholecystitis: Bayesian analysis of data.
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Acute Disease↗
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Results for “Bayesian analysis”
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[Dynamic ECG and coronarography in the diagnosis of ischemic cardiopathy: Bayesian analysis of the probability].
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Adult↗
[Dyspepsia. Bayesian analysis of symptoms as an aid in clinical diagnosis].
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Bayes Theorem↗
Meta-analysis for combining Bayesian probabilities.
Bayesian analysis is a method by which the reliability of diagnostic tests can be determined. It produces a probability of a patient having the disease given a positive test result (posterior probability). If more than one study of a given test's diagnostic accuracy is done, then how can we determine which of these studies has produced the most reliable posterior probability? Meta-analysis is a method whereby data from different studies can be combined. This paper proposes that meta-analysis more accurately estimates the true Bayesian posterior probability than other methods of data pooling.
Bayes Theorem↗
Analysing clinical decision analyses.
We present a critical review of aspects of clinical decision analysis which uses an application to screening for familial intracranial aneurysms. The analysis is reported together with methods for assessing decision trees. These methods appear to be powerful checks on the usually rather intuitive way in which decision trees are built. The problem of assessing the uncertainty in the results of a decision analysis is discussed in detail. In practice, sensitivity analysis covers nearly every calculation apart from the standard evaluation of the decision tree. Different forms of sensitivity analysis are distinguished and given appropriate names: influence analysis, threshold analysis, full Bayesian analysis, Bayesian influence analysis, attribute analysis, generalization analysis and scenario analysis. The biostatistical community may well contribute to the much needed methodological improvement in decision analysis and its different forms of sensitivity analysis, especially if prepared to look beyond the standard statistical techniques.
Adult↗
31P NMR Bayesian spectral analysis of rat brain in vivo.
Bayesian spectrum analysis for parameter estimation is a rigorous statistical (non-Fourier-based) method. Herein the Bayesian quadrature NMR model is introduced and applied to analysis of 31P NMR time domain data from in vivo rat brain. Immunity to both the brain spectrum "baseline hump" and the phase twist is demonstrated.
Animals↗
Selected topics in statistical analysis of clinical research.
Statistical analysis usually is employed in the evaluation of clinical research studies. This paper reviews and makes recommendations in three areas frequently overlooked in the conduct of clinical research: power analysis, specification of a priori research hypotheses, and Bayesian analysis. Power analysis determines the number of subjects required to conduct a meaningful study and should be performed during the planning phase. Research and null hypotheses are essential elements of research design and should be specified prior to statistical analysis. Bayesian analysis can be used both to evaluate diagnostic tests and as an alternative to traditional statistical approaches for testing multiple hypotheses. Application of these methods is described and clinical examples are provided. The discussion is nontechnical and is directed toward the clinical researcher.
Aged↗
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.
Adult↗
[Population pharmacokinetic analysis of indocyanine green retention rate at 15 minutes].
Using the population pharmacokinetic (Bayesian) method, we investigated the indocyanine green (ICG) retention rate at 15 minutes (R 15) in patients with liver disease. For the Bayesian analysis, the mean and variance of parameters, the disappearance rate (K) and the distribution volume, were calculated by the one compartment analysis with data of inpatients. The mean value of the disappearance rate (K) was shown to be smaller and the variances of parameters of the patients were larger than normal values reported, so that it was suggested that the studied group included patients with various kinds of liver diseases. Accordingly, the Bayesian analysis was performed using above statistical results. The distribution volumes, calculated from three points measurements by Bayesian method, were estimated smaller and the initial concentrations higher than those obtained by the one compartment analysis. However, the retention rate (R 15) were well correlated the results of one compartment analysis. The Bayesian analysis using one point measurement at 15 minutes, resulted that the values of retention rate (R 15) were shown about 17% lower, but well correlated with the coefficient gamma = +0.9972 to those obtained by usual method. The disappearance rate (K), which calculated simultaneously from the one point analysis, showed the lowest value on liver cirrhosis. So that, the application of Bayesian analysis to the one point measurement of ICG test was useful to clinical evaluation of liver disease.
Bayes Theorem↗
Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis.
PURPOSE: To test a neural network in differentiation of benign from malignant solitary pulmonary nodules. MATERIALS AND METHODS: Neural networks were trained and tested on the characteristics of 318 nodules. Predictive accuracy of the network was judged for calibration and discrimination. Network results were compared with those with a simpler Bayesian method. RESULTS: The Brier score was 0.142 (calibration, 0.003; discrimination, 0.139) for the neural network and 0.133 for the Bayesian analysis (calibration, 0.012; discrimination, 0.121). Analysis of the calibration curve revealed no significant difference (P < .05) between the slope (b = 1.09) and the line of identity (b = 1) for the neural network or the Bayesian analysis. The area under the receiver operating characteristic curve was 0.871 for the neural network and 0.894 for the Bayesian analysis (P < .05). There were 23 and 21 false-positive predictions and 18 and six false-negative predictions for the neural network and Bayesian analysis, respectively. CONCLUSION: The Bayesian method was better than the neural network in prediction of probability of malignancy in solitary pulmonary nodules.
Adult↗
Bayesian approaches in pharmacokinetic decision making.
The theory of Bayesian analysis and its application to therapeutic and pharmacokinetic decision making are discussed. Diagnostic and therapeutic decisions are commonly based on institution, experience, and laboratory information; these decisions reflect varying degrees of uncertainty. Bayesian analysis quantifies the decision process by attaching probabilities to the likelihood of accuracy of each of these decision-making factors to achieve an overall estimate of decision quality. Using Bayesian principles to quantify the probability of efficacy and toxicity associated with serum drug concentrations represents one application of Bayesian theory to enhance therapeutic decisions. The Bayesian approach in pharmacokinetics involves the prediction of pharmacokinetic values, dosage regimens, and serum concentrations for drugs. Beginning with mean population pharmacokinetic parameters, one uses observed serum concentrations in individual patients to modify these parameters through Bayesian analysis to improve the accuracy of future serum concentration predictions. As more clinical pharmacokinetic laboratories and consultation services become familiar with the procedure, Bayesian forecasting promises to expand markedly the sophistication of therapeutic drug monitoring.
Decision Making↗
Application of step-wise discriminant analysis and Bayesian classification procedure in determining prognosis of acute myocardial infarction.
A retrospective study was carried out to assess the feasibility of computer-assisted prognostication by discriminant analysis and the Bayesian classification procedure based on clinical information collected on patients with acute myocardial infarction. The overall accuracy was 94.2% in predicting hospital death but the prediction of late death after discharge was less accurate. It was found that not all of the 44 variables used for analysis were necessary to reach the same level of predictive accuracy--16 to 20 variables would result in almost the identical prediction. The Bayesian classification procedure was applied to estimate probabilities of individual patients belonging to the different prognostic categories.
Bayes Theorem↗
Cardiovascular autonomic dysfunction in multiple sclerosis is likely related to brainstem lesions.
Impairment of cardiovascular autonomic reflexes has been described in multiple sclerosis (MS), and believed reflecting dysfunction of reflex pathways located within the central nervous system. A battery of cardiovascular autonomic tests were performed in 40 patients with definite MS: R-R interval variation test, deep breathing, Valsalva manoeuvre, blood pressure and heart rate responses to standing, sustained handgrip. The results were evaluated by Bayesian analysis, a pattern recognition technique. The patients had also magnetic resonance imaging (MRI) of brain and in 19 subjects of cervical spinal cord. Deep breathing test and sustained handgrip test produced most frequently abnormal results (17.5% and 40%, respectively). However, only 4 patients (10% had two or more tests abnormal, with a very variable pattern. Evaluation by Bayesian analysis revealed 7 patients (17.5%) with definite autonomic dysfunction. A correlation was found between the confidence level obtained by Bayesian analysis, as index of autonomic function, and the Kurtzke brainstem FS score (r = 0.43, P < 0.01). There was a significant association between presence of autonomic dysfunction and clinical (P < 0.02) and MRI (P < 0.005) evidence of brainstem lesions.
Adult↗
Bayesian probability analysis: a prospective demonstration of its clinical utility in diagnosing coronary disease.
One hundred fifty-four patients referred for coronary arteriography were prospectively studied with stress electrocardiography, stress thallium scintigraphy, cine fluoroscopy (for coronary calcifications), and coronary angiography. Pretest probabilities of coronary disease were determined based on age, sex, and type of chest pain. These and pooled literature values for the conditional probabilities of test results based on disease state were used in Bayes' theorem to calculate posttest probabilities of disease. The results of the three noninvasive tests were compared for statistical independence, a necessary condition for their simultaneous use in Bayes' theorem. The test results were found to demonstrate pairwise independence in patients with and those without disease. Some dependencies that were observed between the test results and the clinical variables of age and sex were not sufficient to invalidate application of the theorem. Sixty-eight of the study patients had at least one major coronary artery obstruction of greater than 50%. When these patients were divided into low-, intermediate-, and high-probability subgroups according to their pretest probabilities, noninvasive test results analyzed by Bayesian probability analysis appropriately advanced 17 of them by at least one probability subgroup while only seven were moved backward. Of the 76 patients without disease, 34 were appropriately moved into a lower probability subgroup while 10 were incorrectly moved up. We conclude that posttest probabilities calculated from Bayes' theorem more accurately classified patients with and without disease than did pretest probabilities, thus demonstrating the utility of the theorem in this application.
Angiography↗
Bayesian hierarchical analysis of within-units variances in repeated measures experiments.
We develop hierarchical Bayesian models for biomedical data that consist of multiple measurements on each individual under each of several conditions. The focus is on investigating differences in within-subject variation between conditions. We present both population-level and individual-level comparisons. We extend the partial likelihood models of Chinchilli et al. with a unique Bayesian hierarchical framework for variance components and associated degrees of freedom. We use the Gibbs sampler to estimate posterior marginal distributions for the parameters of the Bayesian hierarchical models. The application involves a comparison of two cholesterol analysers each applied repeatedly to a sample of subjects. Both the partial likelihood and Bayesian approaches yield similar results, although confidence limits tend to be wider under the Bayesian models.
Algorithms↗
Bayesian derived predictions for twice daily theophylline under outpatient conditions and an assessment of optimal sampling times.
1. The accuracy of a computerised method of pharmacokinetic interpretation of a single serum theophylline concentration, employing the statistical technique of Bayesian analysis, has been evaluated for an oral slow release form of theophylline using twice daily dosing. 2. Twenty-four hour steady state serum theophylline concentration-time profiles of one Uniphyllin Continus 400 mg tablet (Napp Laboratories) every 12 h were measured in 15 patients. These profiles demonstrated a diurnal variation of theophylline absorption which was faster during the day. 3. Revised predictions of the profiles were generated by Bayesian analysis using a single serum theophylline concentration taken during a previous outpatient appointment. Comparing the predicted and measured profiles, the accuracy of the Bayesian method is considered more than adequate for clinical purposes. 4. The predictions produced by the revised estimates were statistically less biased and more precise than those derived by a theophylline algorithm using population data. 5. The mean prediction errors of the revised estimates of the day and night-peak drug concentrations were -0.55 mg l-1 and -0.21 mg l-1 whilst those of the evening and morning troughs were 1.17 mg l-1 and 0.41 mg l-1, respectively. 6. Analysis of the predictive and relative performance of the samples drawn during the profile revealed that the sample taken prior to a morning dose produced the most accurate predictions. 7. There was no statistical difference in the relative predictive performance of samples drawn up to 4 h before or 2 h after the morning dose. It is, therefore, recommended that all serum theophylline concentrations to be used in Bayesian analysis, should be drawn within this period.
Administration, Oral↗
Comparison of the multivariate analysis and CADENZA systems for determination of the probability of coronary artery disease.
The accuracy of 2 discriminate systems for diagnosis of coronary artery disease (CAD), multivariate analysis (MVA) and Bayesian analysis (CADENZA), was evaluated in 113 patients undergoing electrocardiographic stress testing and coronary angiography. MVA uses weighting factors (F values) generated from our patient data, whereas CADENZA uses probabilities gleaned from an extensive review of the American literature. Overall accuracy was similar. MVA had a higher sensitivity for 1-vessel CAD (75 versus 33%), but CADENZA was better for determining the severity of CAD. The 2 systems provided posterior probabilities for disease that were highly correlated (r = 0.56; p less than 0.001). Both systems suggest the need for further testing based on the probability generated; herein lies their major strength. The application of such systems should help the clinician reach a diagnosis or make a decision as to management in a cost-effective manner.
Bayes Theorem↗
Genetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores.
OBJECTIVES: Incisional hernia (IH) affects 13-30% of people after abdominal surgery, resulting in substantial morbidity and costs. While clinical risk factors have been studied extensively, genomic risk for IH is incompletely understood. We aimed to evaluate the impact of polygenic risk scores (PRS) on IH risk prediction. METHODS: We created and evaluated three PRS for abdominal hernia, ventral hernia and latent hernia susceptibility for prediction of IH in an institutional biobank. The primary outcome was defined as the diagnosis or repair of an IH based on ICD-9/10-CM/PCS and CPT codes. Clinical covariates included age, sex, body mass index (BMI), smoking status, index procedure type, and perioperative surgical site infection. A phenome-wide association study (PheWAS) was performed to assess clinical associations with increased PRS. We then tested the ability of the PRS to improve prediction for IH by modeling clinical covariates with and without PRS in patients who underwent abdominal surgery. Model performance was assessed using 10 iterations of 5-fold cross-validation to estimate Brier scores and area under the receiver operating characteristic curve (AUROC), which were compared using cross-model Bayesian analysis of variance. RESULTS: In 55,809 subjects, assessed PRS was significantly associated with incisional, umbilical, and ventral hernia on PheWAS, with 1.19 greater odds of developing IH per 1-SD increase in PRS (95% CI: 1.13-1.25, P < 0.001). Of 9,909 subjects who underwent qualifying abdominal surgery, 706 developed IH. In this cohort, the latent hernia susceptibility PRS was associated with a 16% increased hazard of developing IH per 1-SD increase (HR 1.16; 95% CI: 1.07-1.26; P < 0.001). Compared to a predictive model using clinical covariates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC = 0.660, 95% CI: 0.653-0.666), addition of the PRS showed similar Brier score and AUROC estimates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC: 0.667, 95% CI: 0.661-0.673) at five years. Cross-model Bayesian analysis demonstrated >99% probability of practical equivalence when trying to detect a difference of ≥ 0.02. CONCLUSION: All three PRS for hernia were independently associated with IH, suggesting that genomic factors contribute significantly to IH development. However, none of the three PRS meaningfully improved clinical IH risk prediction in patients who underwent abdominal surgery. This suggests that clinical comorbidities and surgical techniques may be equally as important as genomic architecture.
Bayesian analysis↗