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Results for “Bayesian analysis”

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At least 217 records · Page 12Linked to original sources
PubMed1994

Extending a Bayesian analysis of the two-period crossover to allow for baseline measurements.

In the context of a two-period crossover study with baseline measurements a graphical method is developed for displaying the dependence of posterior inferences concerning the treatment effect on unavoidable prior beliefs about the 'correct' model. The method is a generalization of the approach developed by Grieve for the corresponding case without baselines.

Administration, Cutaneous↗
PubMed1995

A Bayesian analysis of bivariate survival data from a multicentre cancer clinical trial.

I present an analysis of data from a clinical trial of new chemotherapies for colorectal cancer. I model participating clinical sites via random effects, to allow for heterogeneous patient responses across centres. Patient response is measured by both progression and survival times. The data suggest that patient responses are homogeneous before, but heterogeneous after, disease progression. For one new therapy, an increase in efficacy relative to standard therapy is detected before, but not after, progression.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1993

The use of Bayesian analysis of PCR-derived genomic DNA sequences to enable a biologically relevant interpretation of immunoglobulin variable region gene expression.

We analyzed by means of polymerase chain reactions (PCRs) and DNA sequencing techniques the immunoglobulin heavy chain variable region genes of bone marrow B lineage cells. We first formulated an explanatory model to guide understanding of the biological mechanisms determining both the size of the total available pool of relevant genes and clonal expansion following heavy chain gene rearrangement. We then followed Box's paradigm of criticism and estimation to interpret our experimental findings.

Base Sequence↗
PubMed1995

Dependent masking and system life data analysis: Bayesian inference for two-component systems.

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.

Bayes Theorem↗
PubMed1987

Prediction of 1-year outcome after complicated and uncomplicated myocardial infarction: Bayesian analysis of predischarge exercise test results in 300 patients.

After myocardial infarction (MI), the additive prognostic value of exercise variables to clinical variables has been questioned. The merits of a symptom-limited predischarge exercise test were therefore evaluated in clinically defined subgroups of patients. Exercise tests were consecutively performed by 208 survivors of uncomplicated MI (no heart failure, postinfarction angina, recurrent infarction, or late arrhythmias) and by 92 survivors of complicated MI. After uncomplicated MI (1-year mortality rate 4%), an achieved workload greater than 70% of age-predicted maximum identified 145 patients at very low risk (predictive value for survival 98%). After complicated MI (1-year mortality rate 13%), an exaggerated heart rate response was the best predictor of outcome, but had low (92%) predictive value of survival at 155 bpm. It is concluded that stress testing has only limited value after complicated MI. After uncomplicated MI, exercise variables are extremely helpful in identifying patients at very low risk in whom further investigations are not warranted.

Adult↗
PubMed1984

Bayesian analysis using Fourier transforms of thallium-201 scintiscans to predict the presence of coronary artery disease.

Bayes' theorem of conditional probability was applied to the diagnosis of coronary artery disease (CAD) using thallium-201 scintigraphy as the testing procedure. Thallium-201 scintiscans were evaluated with a discriminant function previously developed using the amplitude coefficients of the Fourier transforms of the scans. The technique was applied prospectively to a population of 100 patients undergoing diagnostic coronary arteriography and thallium-201 scintigraphy, including 83 patients with CAD (70% or greater stenosis of luminal diameter) and 17 control subjects. A pretest probability of CAD was determined for each patient from the patient's age, sex and anginal symptoms. The pretest probability was combined with the patient's discriminant score to determine a posttest probability for CAD. For patients with CAD, the mean posttest probability was 0.85. Moreover, 57 of 83 patients (69%) had posttest probabilities exceeding 90%, including 40 patients (48%) with posttest probabilities exceeding 99%. For control subjects, the mean posttest probability was 0.19, with 11 of 17 (65%) having a posttest probability of less than 10%. Overall, 68 subjects had a posttest probability either less than 10% or more than 90% of which 63 were correctly classified (93%). Using a 50% posttest probability as a cutoff for classification, the technique has an 89% sensitivity, an 82% specificity and an overall accuracy of 88%. Therefore, this method objectively distinguishes patients with CAD from control subjects and provides a measure of the certainty of diagnosis. In addition, the discriminant function avoids the problem of inter- and intraobserver variability in visually interpreting thallium-201 scans.

Adult↗
PubMed1995

The utility of three predictors of childhood myopia: a Bayesian analysis.

Any treatment to prevent the onset of juvenile myopia will require predictive tests in order to determine which children should receive treatment. Three risk factors for myopia were evaluated for their ability to predict myopia: (a) refraction at school entry; (b) refraction in infancy; and (c) parental history of myopia. Bayes' theorem was used to estimate these conditional probabilities. Refraction at school entry had twice the power to predict myopia (probability of juvenile myopia given the child is near emmetropia at school entry = 0.53) compared to either infant refraction (0.21-0.28) or parental myopia (0.20-0.25). While a history of any parent having myopia had the highest test sensitivity (probability of a positive family history of myopia given juvenile myopia in the child = 0.90) and refraction at school entry the highest test specificity (probability of more hyperopia than +0.50 D at school entry given no juvenile myopia = 0.91), none of these three factors had high values for both sensitivity and specificity. Further work is required to develop a battery of tests which could predict the onset of juvenile myopia with both adequate sensitivity and specificity.

Age Factors↗
PubMed1995

Bayesian analysis of identification performance in monkey visual cortex: nonlinear mechanisms and stimulus certainty.

The identification performance of single neurons in the primary visual cortex was quantified by measuring how accurately one could know the stimulus based upon the neuron's response. We found that for a typical neuron a response of 10 action potentials, following one brief stimulus presentation, was sufficient to classify the stimulus as belonging to a relatively small region in stimulus space, with a high degree of confidence. The performance was better than that which could be attained through linear summation of excitation and inhibition alone. The results suggest that the enhanced performance is a consequence of two nonlinear mechanisms: contrast gain control and expansive response exponent.

Action Potentials↗
PubMed2006

Teaching Bayesian analysis to Emergency Medicine residents.

Our objective was to determine if a brief didactic would improve Emergency Medicine (EM) resident performance at using a key evidence-based medicine (EBM) concept. We used a prospective, before and after, assessment of EM resident estimates of post-test pulmonary embolism (PE) probability for a defined pre-test probability, computed tomography (CT) and D-dimer results. The survey provided test sensitivity, and specificity for D-dimer and CT. Three months later, residents attended a brief didactic conference on how to use Fagan's Nomogram and likelihood ratios (LRs) to calculate post-test probability of disease. The accuracy of estimates of post-test PE probability was reassessed. The absolute percentage difference in resident estimates from the true post-test PE probabilities decreased from 14.5% (95% confidence interval [CI] 9.7%-19.9%) to 4.5% (95% CI 2.0-6.8%) after the educational intervention. This 10% effect size was statistically significant, p = 0.002. The study demonstrates the efficacy of the lecture method in teaching an EBM concept to EM residents.

Bayes Theorem↗
PubMed1977

Bayesian analysis of electrocardiographic exercise stress testing.

We evaluated the diagnostic accuracy of exercise-induced ST-segment depression in detecting coronary-artery disease by applying the likelihood-ratio formulation of Bayes's theorem to stress-test data, which were partitioned into half-millimeter ranges of depression. The graphic relation between the predictive value of a given test result and the pretest risk of disease in the test subjects was obtained for each of these half-millimeter intervals. This method reveals that the predictive value of testing depends on the degree of ST-segment depression, and that the pretest risk of coronary-artery disease is an important determinant of the predictive value of any test result in the individual patient. These findings suggest that the use of the terms "positive" and "negative" are inappropriate to describe most stress-test results. Instead, the results should be interpreted in terms of a continuum of risk based on the extent of ST-segment depression.

Adult↗
PubMedDate not supplied

Recall and recognition as diagnostic indices of Malignant Memory Loss in Senile Dementia: a Bayesian analysis.

The Benign Senescent Forgetfulness of normal aging and the Malignant Memory Loss of Senile Dementia of the Alzheimer Type (SDAT) each have a distinct symptomatology, course, and prognosis. The purpose of this study was to evaluate the discriminative validity and relative predictive values of recall and recognition as diagnostic screening tests for the Malignant Memory Loss of SDAT. Thirty-six patients with mild to moderate SDAT and 40 normal aged controls were studied. Both recall and recognition showed good discriminative validity. However, analysis of recall and recognition by Bayes's Theorem revealed the relative predictive values as diagnostic screening instruments were 11% and 100% respectively. Thus, it was concluded that while both recall and recognition have discriminative validity under experimental conditions, a test of recognition is the preferred diagnostic instrument when screening for the Malignant Memory Loss of SDAT.

Aged↗