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Results for “Bayesian analysis”

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PubMed1990

[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↗

PubMed1976

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↗

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↗

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 ≥ 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↗

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↗

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↗

PubMed1987

Expert systems in psychiatry. A review.

Existing computer-based decision aids in the areas of psychiatric diagnosis and consultation are reviewed, and the prospects for expert system development within the mental health field are discussed. Emphasis is placed upon the decision-making models used in these systems rather than on their particular application area. The decision-making paradigms discussed are (1) data bank analysis, (2) statistical pattern recognition, (3) Bayesian analysis, (4) logical flow chart method, and (5) knowledge-based (expert system) approaches. For each paradigm, its essential features, its strengths and weaknesses, and some example applications are presented.

Diagnosis, Computer-Assisted↗

PubMed1990

Bayesian versus Fourier spectral analysis of ion cyclotron resonance time-domain signals.

The frequency-domain spectrum obtained by Fourier transformation (FT) of a time-domain signal is accurate only for a continuous noiseless time-domain signal of infinite duration. For discrete noisy truncated time-domain signals, non-FT (e.g., Bayesian analysis) methods may provide more accurate spectral estimates of time-domain signal frequencies, relaxation time(s), and relative abundances. In this paper, we show that Bayesian analysis of simulated and experimental ion cyclotron resonance (ICR) time-domain noisy signals can produce a spectrum with mass accuracy improved by a factor of 10 or more over that obtained from a magnitude-mode discrete fast Fourier transform (FFT) spectrum. Moreover, Bayesian analysis offers the useful advantage that it automatically estimates the precision of its iteratively determined spectral parameters. The main disadvantage of Bayesian analysis is its lengthy computation time compared to that of FFT (hours vs seconds on the same hardware for approximately 4K time-domain data points); the Bayesian computation time increases rapidly with the number of spectral peaks and (less rapidly) with the number of time-domain data points. Bayesian analysis should thus prove useful for those FT/ICR applications involving relatively few data points and/or requiring high mass accuracy.

Bayes Theorem↗

PubMed1991

Multivariate analysis of cardiovascular reflexes applied to the diagnosis of autonomic neuropathy.

A battery of cardiovascular reflex tests is usually performed for the diagnosis of autonomic neuropathy. The tests discriminate well between normal and definitely abnormal autonomic function. However, in some patients the results are borderline and their autonomic status cannot be better defined. We performed multivariate statistical analysis of six cardiovascular autonomic tests with the aim of increasing their diagnostic efficiency. Eighty-five healthy subjects and 95 patients at risk for autonomic neuropathy were studied. Principal component analysis and two pattern recognition methods, the Bayesian technique and the SIMCA method, were applied. It was found that: (1) normal models obtained by Bayesian analysis showed very high specificity and sensitivity; (2) a battery of two tests for parasympathetic function (R-R interval variation test, deep breathing) and two tests for sympathetic function (blood pressure responses to standing and to sustained handgrip) provide an appropriate diagnostic approach, if multivariate analysis is used; (3) multivariate analysis allows a more precisely defined assessment of autonomic nervous system function in so-called borderline patients.

Adult↗