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PubMed1984

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↗
PubMed1989

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↗
PubMed1984

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↗
PubMed2026

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 &#x2265; 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↗
PubMed1991

[An effective method for the estimation and comparison of the ED50 with small sample sizes].

In ED50 experiments the relationship between dose and probability of response is often modelled by the probit function. Standard statistical analysis estimates the parameters of this function by the maximum likelihood principle and derives the ED50 and its fiducial limits from these parameters. Bayesian analysis is more effective in two respects: It optionally includes prior information and in all but very few instances yields confidence intervals, whereas fiducial intervals often cannot be determined. Bayesian analysis of experiments with one substance has been treated in GRIEVE (1988). In the present article the mathematically interested reader is shown how to compare two substances. The probability of higher ED50 in the one substance as well as estimates of the ratio of the ED50's are obtained. The methods are easily extended to the effective dose for any other reasonable percentage of animals, e.g. ED90 or ED25. Experiments concerning lethal doses can be analysed by these methods as well. Both types of analysis are applied in two examples which compare new batches of vaccines with an established standard. In the first example both substances are nearly equivalent, while in the second example the new batch is considerably more efficient. An interactive FORTRAN program for a personal computer is available (cf. last section of 5.). It computes the maximum likelihood and the Bayesian solution, using approximate formulas in the latter case. Due to these approximations it was possible to develop a Bayesian program which is fast enough to run on a PC. Validation procedures have been performed. The output consists of a print file and, optionally, an ASCII file containing the coordinates of the posterior probability density and distribution functions.

Animals↗
PubMed1989

Bayesian regression analysis of non-steady-state phenytoin concentrations: evaluation of predictive performance.

Michaelis-Menten saturable pharmacokinetics confound the determination of appropriate phenytoin maintenance doses. This study retrospectively evaluated the performance of an IBM-PC/XT computer program applying Bayesian regression to the "explicit solution to the Michaelis-Menten equation." Zero to five non-steady-state phenytoin serum concentrations were used to predict either non-steady-state concentrations at least 10 days in the future (n = 49) or steady-state concentrations (n = 20). Non-steady-state concentration prediction precision (% mean absolute error) using 0-5 non-steady-state feedbacks was 137%, 62%, 39%, 31%, 25%, and 15%, respectively, and steady-state concentration prediction precision was 446%, 47%, 50%, 44%, 21%, and 13%, respectively. Elimination of subjects receiving concurrent drugs known to induce phenytoin metabolism significantly improved predictions based on population priors; however, performance improvements were not apparent after two serum level feedbacks. The program provided clinically acceptable predictions with four or more feedbacks. Refinement of population parameters and optimal sampling times should further improve performance.

Aged↗
PubMed1988

Study and performance evaluation of statistical methods in image processing.

Two statistical image processing formalisms involving the entropy concept and Bayesian analysis are studied. Iterative imaging algorithms of the formalisms are formulated by employing, for the purpose of performance evaluation and easy implementation, the steepest descent method for the solution of entropy concept and the expectation maximization technique for the solution of Bayesian analysis. Quantitative evaluation and comparison of the convergence performance of the iterative algorithms on computer generated ideal and experimental radioisotope phantom imaging noisy data are given. The study concludes that the entropy algorithm can converge relatively fast, but it is very sensitive to noise in measured data due to the ill-posed nature of inverse problems and its lack of ability to consider the statistics of data fluctuation; while the Bayesian algorithm converges monotonically even with noisy data and has the advantage of considering both the a priori source distribution information and the statistical fluctuation of measured data.

Algorithms↗
PubMed1987

The accuracy of a pharmacokinetic theophylline predictor using once daily dosing.

1. The accuracy of a computer based pharmacokinetic prediction method based on Bayesian analysis has been evaluated for an oral show release form of theophylline. 2. In 83 patients from seven centres 24 h serum theophylline concentration-time profiles were measured under a variety of circumstances. 3. Revised predictions of 24 h serum theophylline concentration profiles were generated by Bayesian analysis using single serum drug concentrations taken before, during and after the study days in different subgroups of those patients. Comparing the predicted and measured profiles the mean prediction error (bias) was 0.05 mg l-1 for peak concentrations and 0.04 mg l-1 for trough concentrations during once daily dosing. The corresponding root mean squared prediction errors (precision) were 2.59 and 1.17 mg l-1, respectively. 4. This accuracy is considered more than adequate for clinical purposes. 5. The technique can be used with a variety of other drugs and can form a valuable part of a routine therapeutic drug monitoring service.

Adult↗
PubMedDate not supplied

[Evaluation of the activity of creatine phosphokinase for the detection of carriers of Duchenne-type muscular dystrophy in families in the city of Monterrey, Mexico].

The activity of serum creatine phosphokinase (CPK) was determined in 80 female members of 23 families with affected members of Duchenne type muscular dystrophy (DMD) and compared with the values of a control group of 100 unaffected women. The control group values exhibited a normal distribution of frequency with a mean of 21 U/L and standard deviation from the mean of 7.9 U/L. Sixty nine percent (11/16) of obligatory carriers showed CPK values higher than the mean of the control group plus two standard deviations of the mean. Thirty one percent (5/16) had false negative values. These percentages are similar to those reported in other studies. Elevated CPK activity was found in 45% (18/40) of type A possible carriers (relatives of obligatory carriers) and the group of possible carriers type B (mothers and relatives of isolated cases) 42% (10/24) exhibited high CPK values. Bayesian analysis was also used in all possible-carriers (A and B). We also report an estimation of the fertility of the DMD gene carriers and of their attitude towards family planning. It is concluded that the determination of serum CPK activity, despite its shortcomings, associated with Bayesian analysis when necessary, could be the method of choice for quick and inexpensive evaluation of the carrier status, mainly in families with members affected by DMD.

Creatine Kinase↗
PubMed1992

Bayesian subset analysis in a colorectal cancer clinical trial.

Subset analysis is the examination of treatment comparisons within groups of patients with restricted levels of patient characteristics. Such analyses are vulnerable to multiplicity effects. We examine the problem in the context of a proportional hazards model with terms for treatment, each of several dichotomous covariates representing the patient characteristics of interest, and treatment-by-covariate interaction effects. Parametrically, a subset-specific treatment effect is equal to the treatment effect term plus a linear combination of the interaction terms. We present Bayesian point and interval estimates under the assumption that the interaction terms are exchangeable and the prior distributions for the other regression parameters are locally uniform. This produces a shrinking of the estimated interaction effects towards zero, thereby discounting them and dealing in a natural way with multiplicity. We illustrate the method using results of a recent North Central Cancer Treatment Group/Mayo Clinic study in advanced colorectal cancer.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1988

The accuracy and stability of Bayesian theophylline predictions.

Pharmacokinetic parameters for theophylline were determined in 33 patients (3 women), mean age 61.2 years and weight 74.6 kg using the following three methods: (a) standard one-compartmental model calculations, assuming 100% bioavailability, after a single dose of theophylline syrup (mean dose 413 mg); (b) drug nomogram; and (c) Bayesian analysis. Patients entered a randomised study of three two-monthly dosage regimens using low, medium, and high theophylline twice daily doses. These doses produced mean (+/- SE) steady-state serum theophylline concentrations of 6.3 (+/- 0.4), 12.1 (+/- 0.3) and 18.3 (+/- 0.5) mg/L, respectively. A fourth period of placebo (2-month duration) was also included. At the end of each treatment period the measured serum theophylline concentration of each patient was compared with those predicted by each of the above three methods. The revised estimates derived from Bayesian analysis produced the least biased [mean prediction error (ME)] and most precise (mean squared prediction error) predictions for all three dosage periods. Statistical analysis of relative performance demonstrated that the difference in precision between the revised estimates and those of the other two methods was significant (p less than 0.05) with the magnitude of the difference increasing with dose. The revised estimates were also found to be less biased (p less than 0.05) than those of the nomogram. The ME (+/- SE) of the revised estimates for the low, medium, and high dosage periods was 0.34 (+/- 0.30), -0.02 (+/- 0.22) and -0.48 (+/- 0.31) mg/L, respectively.

Bayes Theorem↗
PubMed1991

An assessment of population-based and Bayesian methods to individualize digoxin doses shortly after the start of therapy for atrial fibrillation.

The accuracy of population-based methods and of Bayesian analysis to predict individual digoxin pharmacokinetic variables have been evaluated by their ability to predict a measured peak and trough serum digoxin concentration. We studied 13 digitalized patients (three women) whose mean (range) age and weight was 65.8 (60-78) years and 76.6 (68-101.6) kg and who had stable renal function. The population-based methods (using a clearance of 48.87 + 0.87 x creatinine clearance in ml/h/kg and volume of distribution, in litres, of either 7.3 x weight (kg) or 269 + 3.12 x creatinine clearance) were more than adequate for clinical purposes. The mean prediction errors of a measured steady-state peak concentration from these two population methods were -0.074 and 0.013 microgram/l respectively, whilst those of a measured trough concentration were -0.058 and 0.005 microgram/l. Bayesian analysis, using a sample drawn 11 h after the dose on day five of therapy, gave overall the least biased and most precise of the revised estimates. The mean prediction errors of peak and trough values using this sample were 0.069 and -0.005 microgram/l respectively. As expected, the closer the sample was drawn to the time of the trough concentration the more precise were the Bayesian-derived predictions. The value of the Bayesian technique to individualize digoxin doses could not be validated because it was not possible to distinguish between this and the population methods.(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗
PubMed1991

The prediction of steady-state plasma phenobarbitone concentrations (following low-dose phenobarbitone) to refine its use as an indicator of compliance.

1. A model for predicting the steady-state plasma concentration of phenobarbitone following low-dose phenobarbitone used as an indicator of compliance was derived using data for 10 healthy volunteers. 2. Each volunteer was given a single 30 mg oral dose of phenobarbitone and the pharmacokinetics were described. Subsequently, volunteers were given phenobarbitone 2 mg daily for 28 days and a further pharmacokinetic profile determined during and after this period. 3. An initial predicted estimate of steady-state plasma drug concentration was made using each volunteer's demographic details. This estimate was revised by Bayesian analysis using single timed samples (24, 48, 72 or 96 h) following the single dose. 4. The model was tested on a further 10 healthy volunteers given a single 8 mg dose and who were subsequently given 2 mg daily for 28 days. 5. The revised estimate of peak steady-state plasma phenobarbitone concentration utilising the 96 h post-single dose concentration (356 ng ml-1) was least biased (mean prediction error +/- 95% CI = 10.6 +/- 19.8 ng ml-1) and most precise (root mean square error +/- 95% CI = 28.3 +/- 19.0 ng ml-1). In all cases the peak or trough steady-state drug concentration was within 13% of the predicted value. 6. The model reflected compliance accurately in a further eight volunteers with simulated partial (two-thirds) compliance. 7. The use of a predictive model using Bayesian analysis to estimate expected steady-state plasma phenobarbitone concentrations could increase further the usefulness of low-dose phenobarbitone as an indicator of compliance.

Adult↗
PubMed1983

Electrocardiographic recognition of left atrial enlargement.

The ECG is widely used as a screening test for left atrial enlargement (LAE). Surprisingly, the most widely used criterion of LAE, the P-terminal force in lead V1 (PTF-V1) has not been systematically evaluated to determine the optimal level of PTF-V1 for detection of LAE in clinical populations. Accordingly, we examined the relationship between PTF-V1 and left atrial size by echocardiogram in 361 patients and performed a Bayesian analysis of test performance in populations with a varying prevalence of LAE. As PTF-V1 increased from greater than or equal to 0.03 to greater than or equal to 0.08, sensitivity in the 82 patients with LAE (LA dimension greater than 40 mm) fell from 51% to 23%, and specificity rose from 70% to 93%. In our study population (LAE prevalence = 23%), diagnostic performance of criteria was: PTF-V1 greater than or equal to 0.03 greater than or equal to 0.04 greater than or equal to 0.05 greater than or equal to 0.06 greater than or equal to 0.08 Positive Predictive Accuracy 33 46 52 58 50 Negative Predictive Accuracy 83 83 84 83 80 Per Cent Correct Diagnosis 66 76 78 80 77 Positive predictive accuracy and per cent correct diagnosis improved progressively as PTF-V1 rose from greater than or equal to 0.03 to greater than or equal to 0.06, but fell at greater than or equal to 0.08. Applying our sensitivity and specificity data to Bayesian analysis, PTF-V1 greater than or equal to 0.06 performed best in all populations with prevalence of LAE less than or equal to 50%. We conclude that use of PTF-V1 greater than or equal to 0.06 is superior to the standard criterion of PTF-V1 greater than or equal to 0.04 for all purposes ranging from screening of a general population to evaluation of diseased individuals whose likelihood of LAE ranges up to 50%.

Adolescent↗
PubMed1991

Bayesian subset analysis.

As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models. Exchange-ability among the interactions is a key assumption; thus, the results are of interest primarily in the context of examining a collection of subsets with no definite a priori distinction relative to treatment effect. Exchangeability leads to a shrinking of the posterior distributions of the interaction terms toward the natural origin of 0, offsetting the tendency of the estimated effects to disperse. The method is applied to parameter estimates from a proportional hazards regression analysis of survival data from a clinical trial, invoking the approximate multivariate normal distribution of the estimates. No subjective prior distributions are required. Vague priors are used for all of the regression coefficients except the treatment-by-covariate interactions, which are assumed to follow a normal distribution.

Clinical Trials as Topic↗
PubMed1983

Cross-validation of a patient classification procedure: an application of the U method.

The objective of this article is to present the methods used for the validation of a patient classification system that was based on the concept of types of care (PCTC system). The PCTC system was developed to improve placement decisions for long-term care patients and also to provide information required for planning in the field of long-term care. A sample of long-term care patients was selected from various institutions/programs and the patients in the sample were assessed and classified by the program practitioners (users) as well as an independent panel (criterion team) composed of a physician, a nurse, and a social worker, using prototype forms specially designed for the project. An objective and empiric classification model was developed by applying discriminant analysis, Bayesian classification procedure, and cluster analysis techniques. The classification validity was evaluated by the use of the R, H, and U methods.

Adult↗
PubMed1978

Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.

We classified microorganisms from the clinical laboratory by using information provided by the Gram stain and antibiotic sensitivity profiles obtained with the Bauer-Kirby technique. Approximately 4,000 microorganisms, routinely identified and tested for antibiotic sensitivities in a large hospital microbiology laboratory, were used as a data set for several pattern recognition classification methods: K--nearest-neighbor analysis, statistical isolinear multicomponent analysis, Bayesian inference, and linear discriminant analysis. K--nearest-neighbor analysis yielded the highest prospective classification accuracy for gram-negative organisms, 90%. When those organisms displaying an atypical antibiotic resistance pattern were excluded from the data, the gram-negative classification accuracy improved to 95%. These results are inferior to currently accepted biochemical identification methods. Microorganisms with atypical antibiotic resistance patterns are likely to be misidentified and are common enough (17% of our isolates) to limit the feasibility of routine identification of microorganisms from their antibiotic sensitivities.

Anti-Bacterial Agents↗