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PubMed1991

A Monte Carlo method for Bayesian inference in frailty models.

Many analyses in epidemiological and prognostic studies and in studies of event history data require methods that allow for unobserved covariates or "frailties." Clayton and Cuzick (1985, Journal of the Royal Statistical Society, Series A 148, 82-117) proposed a generalization of the proportional hazards model that implemented such random effects, but the proof of the asymptotic properties of the method remains elusive, and practical experience suggests that the likelihoods may be markedly nonquadratic. This paper sets out a Bayesian representation of the model in the spirit of Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221) and discusses inference using Monte Carlo methods.

Algorithms↗
PubMed1992

Advanced computer programs for drug dosing that combine pharmacokinetic and symbolic modeling of patients.

In this paper, we describe our design for advanced drug dosing programs that "reason" using a combination of Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends this previous work by incorporating a Bayesian pharmacokinetic model, performing a "meta-level" analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of this analysis in reasoning for dosing and therapeutic monitoring recommendations. The design has been implemented in a program for aminoglycoside antibiotics called Aminoglycoside Therapy Manager. The program is user-friendly and runs on low-cost general-purpose hardware. The initial validation study showed that the program was as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software and that its dosing recommendations were similar to those of an expert.

Aminoglycosides↗
PubMed1996

Bayesian meta-analysis, with application to studies of ETS and lung cancer.

Meta-analysis enables researchers to combine the results of several studies to assess the information they provide as a whole. It has been used to give a systematic overview of many areas in which data on a possible association between an exposure and an outcome have been collected in a number of studies but where the overall picture remains obscure, both as to the existence or size of the effect. This paper outlines some innovations in meta-analysis, based on using Markov chain Monte Carlo (MCMC) techniques for implementing Bayesian hierarchical models, and compares these with a more well-known random effects (RE) model. The new techniques allow different aspects of variation to be incorporated into descriptions of the association, and in particular enable researchers to better quantify differences between studies. Both the classical and Bayesian methods are applied, in this paper, to the current collection of studies of the association between incidence of lung cancer in female never-smokers and exposure to environmental tobacco smoke (ETS), both in the home through spousal smoking and in the workplace. In this paper it is demonstrated that compared with the RE model, the Bayesian methods: (a) allow more detailed modeling of study heterogeneity to be incorporated; (b) are relatively robust against a wide choice of specifications of such information on heterogeneity; (c) allow for more detailed and satisfactory statements to be made, not only about the overall risk but about the individual studies, on the basis of the combined information. For the workplace exposure data set, the Bayesian methods give a somewhat lower overall estimate of relative risk of lung cancer associated with ETS, indicating the care that needs to be taken in using point estimates based on any one method of analysis. On the larger spousal data set the methods give similar answers. Some of the other concerns with meta-analysis are also considered. These include: consistency between different geographic areas (Asia and the United States), and our studies show that Bayesian methods permit an account of the overall picture to be taken, thus improving the ability to estimate accurately in the subgroups; and publication bias which, as shown with the spousal exposure data, may lead to an inflated excess risk.

Air Pollution, Indoor↗
PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗
PubMed1996

Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance component inference.

A set of FORTRAN programs to implement a multiple-trait Gibbs sampling algorithm for (co)variance component inference in animal models (MTGSAM) was developed. The MTGSAM programs are available to the public. The programs support models with correlated genetic effects and arbitrary numbers of covariates, fixed effects, and independent random effects for each trait. Any combination of missing traits is allowed. The programs were used to estimate variance components for 50 replicates of simulated data. Each replicate consisted of 50 animals of each sex in each of four generations, for 400 animals in each replicate for two traits. For MTGSAM, informative prior distributions for variance components were inverted Wishart random variables with 10 df and means equal to the simulation parameters. A total of 15,000 Gibbs sampling rounds were completed for each replicate, with 2,000 rounds discarded for burn-in. For multiple-trait derivative free restricted maximum likelihood (MTDFREML), starting values for the variance components were the simulation parameters. Averages of posterior mean of variance components estimated using MTGSAM with informative and flat prior distributions for variance components and REML estimates obtained using MTDFREML indicated that all three methods were empirically unbiased. Correlations between estimates from MTGSAM using flat priors and MTDFREML all exceeded .99.

Algorithms↗
PubMed2025

Genomic prediction and genome-wide association study for liver abscesses in crossbred beef cattle.

Liver abscesses are a concern in feedlot cattle, and little is known about the role of genetics in their development. This study aimed to estimate genetic parameters and to identify single-nucleotide polymorphisms (SNPs) associated with liver abscesses. Crossbred cattle representing 18 breeds in the U.S. Meat Animal Research Center Germplasm Evaluation Program were phenotyped for liver abscesses at slaughter (n&#x2005;=&#x2005;9,044). Seventeen percent of cattle had liver abscesses. These cattle had genotypes that were imputed to sequence variant genotypes. After filtering and quality control, 340,723 SNPs were used in the analysis. Liver abscess prevalence was modeled with a single-step genomic best linear unbiased prediction (ssGBLUP) threshold model using a Bayesian framework. The model included contemporary group (sex, treatment group, and slaughter date), additive genomic, and residual effects. Genomic heritability was 0.039 (95% highest posterior density&#x2005;=&#x2005;0.005, 0.081), which was very small. To assess prediction quality, a 5-fold random cross-validation structure was used. Method Linear Regression was used to assess accuracy, bias, and dispersion by comparing estimated breeding values (EBV) from full and reduced analyses. Cross-validation metrics showed EBV based on genotypes had 0.05 reliability (SD&#x2005;<&#x2005;0.01) with no bias relative to EBV based on genotypes and phenotypes. For the genome-wide association study, SNP effects were back calculated from the EBV solutions from ssGBLUP. No SNPs were associated with liver abscesses at a Benjamini-Hochberg adjusted 0.05 significance level. Although a large dataset was used, this result was because of the low genomic heritability and imprecise EBV used to calculate SNP effects. Based on these results, environmental factors contribute to most of the variation in liver abscesses. Genetic selection to reduce liver abscesses would be slow because of the low genomic heritability, measurement late in life, and inability to measure breeding animals. A faster approach would be finding additional environmental interventions that maintain animal performance.

Animals↗
PubMed1987

Disposition of phenytoin in critically ill trauma patients.

Estimates of phenytoin pharmacokinetic variables and protein binding were determined in 10 adult critically ill trauma patients. Each study subject received phenytoin sodium as an intravenous loading dose of 15 mg/kg, followed by an initial intravenous maintenance dose of 6 mg/kg/day. Serial blood samples were obtained throughout the seven-day study period and analyzed for total and unbound serum phenytoin concentrations. The concentration data for each patients were fitted to a one-compartment model with elimination defined by the Michaelis-Menten constant Km and the maximum rate of metabolism (Vmax) and to a one-compartment model with first-order elimination. The Michaelis-Menten model used Bayesian parameter estimation while the linear model used weighted non-linear least-squares regression analysis. Unbound phenytoin fraction ranged from 0.073 to 0.25. Free fraction increased 7% to 108% in 9 of 10 patients (median increase 29%) from day 1 to day 7 of therapy. Variable estimates using the Michaelis-Menten model were as follows: volume of distribution, 0.76 +/- 0.15 L/kg (0.58-1.01 L/kg); Vmax, 568 +/- 197 mg/day (350-937 mg/day); and Km, 4.5 +/- 1.8 mg/L (1.8-6.2 mg/L). These estimates fell within the wide range of values obtained in studies using stable patients or healthy volunteers. The Michaelis-Menten model was significantly less biased and more precise than the linear model. Three of four patients who continued to receive their study maintenance dose had substantially lower measured total serum concentrations of phenytoin than predicted using the study variable estimates.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMedDate not supplied

Lansoprazole compared with histamine2-receptor antagonists in healing gastric ulcers: a meta-analysis.

To compare the gastric ulcer healing rates of lansoprazole with histamine2-receptor antagonists (H2RAs) (ranitidine, famotidine, cimetidine, and roxatidine), a meta-analysis was performed using data from five published and eight unpublished randomized controlled trials. Analyses were performed using (1) both evaluable patients (n = 1527) and all randomized patients (n = 1655) (assuming that patients lost to follow-up were treatment failures); (2) all studies and a subset of studies that received high methodologic quality scores; and (3) fixed-effects, random-effects, and Bayesian statistical models. In all cases, lansoprazole was associated with a significantly higher rate of endoscopic healing at both 4 and 8 weeks compared with the H2RAs. When the most conservative Bayesian statistical model and intent-to-treat analysis were used, lansoprazole was associated with a 33% higher healing rate at 4 weeks (risk ratio = 1.33; 95% confidence interval [CI] = 1.19 to 1.49) and a 12% higher healing rate at 8 weeks (risk ratio = 1.12; 95% CI = 1.06 to 1.19) than were the H2RA agents. Similar results were obtained when the meta-analysis was performed on evaluable rather than all randomized patients and using the three different analytical techniques noted above. Slightly lower, though still highly significant, improvement in ulcer healing rates was obtained when the meta-analysis was performed using a subset of six studies that received high methodologic quality scores. These results support the conclusion that lansoprazole heals ulcers more quickly than do the H2RAs and also achieves higher overall rates of healing. The eradication of Helicobacter pylori associated with gastric ulcers was not assessed in individual studies.

2-Pyridinylmethylsulfinylbenzimidazoles↗
PubMed1996

A comparison of three techniques for rapid model development: an application in patient risk-stratification.

Accurately risk-stratifying patients is a key component of health care outcomes assessment. And, many health care organizations increasingly are relying upon automated means for assistance in making patient risk-stratification decisions. Unfortunately, the process of outcome model development, as it is currently practiced, is both time consuming and difficult. We investigated the relative abilities of three modeling techniques (logistic regression, artificial neural network (ANN), and Bayesian) to rapidly develop models for risk-stratifying patients. Our results demonstrated that all three modeling techniques perform equally well in certain situations. However, the Bayesian model with conditional independence had the best overall performance. Unfortunately, none of the models were able to achieve the degree of accuracy which would be required in a medical setting.

APACHE↗
PubMed1998

Issues in Bayesian Analysis of Neural Network Models

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed&hyphenarchitecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data&hyphendriven procedure to identify sensible architectures.

Journal Article↗
PubMed1996

Validity of Bayesian forecasting programme in therapeutic drug monitoring of vancomycin in a surgical intensive care unit: a prospective evaluation.

The objectives of this study were: (a) to assess the predictive performance of a 2-compartment Bayesian vancomycin forecasting programme in critically ill patients in the surgical intensive care unit, and (b) to show the applicability of the programme, which is based on parameters derived from Western population, in our local Asian population. Twenty critically ill patients were enrolled into the study programme. All patients received multiple-dose vancomycin for infections due to methicillin-resistant staphylococcus aureus (MRSA). The patients dosage regimen were optimised by entering a set of peak and trough vancomycin serum levels into a clinical computer program (MB; USC*PACK PC collection; University of Southern California, USA) by utilising a 2-compartment Bayesian population model. The correlation between observed and predicted serum peak and trough concentrations were evaluated for both the non-fitted and fitted models by linear regression analysis. There was a significant correlation between observed and predicted concentrations using the fitted model (r = 0.97, P < 0.05). There was no significant correlation of these concentrations in the non-fitted model (r = 0.8). This study shows that the Bayesian programme is able to accurately predict future vancomycin concentrations in our local Asian population. It is possible to optimise patients dosage regimens with the knowledge of two concentrations of vancomycin in order to achieve targeted therapeutic goals.

Aged↗
PubMed1985

A Bayesian approach to nonlinear random effects models.

Nonlinear random effects models are considered from the Bayesian point of view. The method of analysis follows closely that of Lindley and Smith (1972, Journal of the Royal Statistical Society, Series B 34, 1-42). The numerical method is related to the EM algorithm.

Analysis of Variance↗
PubMed1997

A flexible approach to time-varying coefficients in the Cox regression setting.

Research on methods for studying time-to-event data (survival analysis) has been extensive in recent years. The basic model in use today represents the hazard function for an individual through a proportional hazards model (Cox, 1972). Typically, it is assumed that a covariate's effect on the hazard function is constant throughout the course of the study. In this paper we propose a method to allow for possible deviations from the standard Cox model, by allowing the effect of a covariate to vary over time. This method is based on a dynamic linear model. We present our method in terms of a Bayesian hierarchical model. We fit the model to the data using Markov chain Monte Carlo methods. Finally, we illustrate the approach with several examples.

Bayes Theorem↗
PubMed1997

Large sample Bayesian inference on the parameters of the proportional hazard models.

This paper considers large sample Bayesian analysis of the proportional hazards model when interest is in inference on the parameters and estimation of the log relative risk for specified covariate vectors rather than on prediction of the survival function. We use a normal prior distribution for the parameters and make inferences based on the derived posterior distribution. The suggested approach is much simpler than alternative Bayesian analyses previously suggested for the proportional hazards models. Using simulated data we compare estimates obtained from the Bayesian analysis with those obtained from the full proportional hazards model and the reduced model after backwards elimination. We show that under a wider range of assumptions, the Bayesian analysis provides reduced estimation errors and improved rejection of noise variables. Finally, we illustrate the methodology using data from a large study of prognostic markers in breast cancer.

Bayes Theorem↗
PubMed1994

The hierarchical Bayesian approach to population pharmacokinetic modelling.

Compartmental models are widely used to model the profile of drug concentrations versus time from administration in an individual subject. Observed concentrations are then modelled as noisy departures from the underlying profile, the latter characterised for each individual by a small number of 'individual parameters'. When a population of individuals is studied, inter-individual variation is modelled by assuming that the individual profile parameters are drawn from a population distribution, the latter characterised by 'population parameters' describing, in effect, a mean population profile and individual variation around it. From a Bayesian statistical perspective, such models fit exactly into the so-called hierarchical modelling framework, which provides a coherent basis for individual and population inferences and prediction, as well as for decision-making (for example, the design of dosage regimens). This paper outlines the hierarchical model framework and describes how the required computations can be carried out in a straightforward manner by a Markov chain Monte Carlo technique known as Gibbs sampling, even when models involve mean-variance relationships and outliers.

Bayes Theorem↗
PubMed1996

A Bayesian approach to Weibull survival models--application to a cancer clinical trial.

In this paper we outline a class of fully parametric proportional hazards models, in which the baseline hazard is assumed to be a power transform of the time scale, corresponding to assuming that survival times follow a Weibull distribution. Such a class of models allows for the possibility of time varying hazard rates, but assumes a constant hazard ratio. We outline how Bayesian inference proceeds for such a class of models using asymptotic approximations which require only the ability to maximize the joint log posterior density. We apply these models to a clinical trial to assess the efficacy of neutron therapy compared to conventional treatment for patients with tumours of the pelvic region. In this trial there was prior information about the log hazard ratio both in terms of elicited clinical beliefs and the results of previous studies. Finally, we consider a number of extensions to this class of models, in particular the use of alternative baseline functions, and the extension to multi-state data.

Bayes Theorem↗