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A microcomputer program for critical evaluation of diagnostic tests.

We developed a microcomputer program that provides a Bayesian model of diagnostic performance and a simple decision tree model of clinical utility. We have used this program to review diagnostic performance and clinical utility for proposed new services at our 360-bed university hospital. We believe that significant benefits can be achieved if medical journals report complete data on test performance. First, this allows physicians to perform their own evaluations of diagnostic performance. Second, this allows physicians to evaluate clinical utility using either standard decision trees or decision trees that reflect specific clinical problems.

Bayes Theorem↗

[Aid to diagnosis and decision making in an acute abdominal pain syndrome].

Diagnostic and decision making performances of a Bayesian model have been compared with clinically performances in abdominal pain of acute onset. Diagnostic accuracy of computer (63.3 p. cent) was lower than diagnostic accuracy of clinicians (72.6 p. cent). A two-fold increase in the number of diagnoses similarly decreased both performances. When the computer came to use national data base (6 916 patients) instead of local data base (571 patients), diagnostic accuracy of low-prevalence diseases increased and diagnostic accuracy of high-prevalence diseases decreased. Decision making accuracy of computer and clinicians were identical.

Abdomen↗

Measuring quality of care in psychiatric emergencies: construction and evaluation of a Bayesian index.

OBJECTIVE: This study was conducted to determine whether an index for measuring quality of care for psychiatric emergencies is reliable and valid. DATA SOURCES/STUDY SETTING: The study used primary data collected over a 12-month period from two urban hospitals in the Northeast. One had 700 inpatient beds, an inpatient psychiatric unit, and community mental health personnel located in the emergency department. The other had 300 beds but none of the other hospital's features. STUDY DESIGN: The index was developed by a panel of experts in emergency psychiatry using a subjective Bayesian statistical methodology and was evaluated in terms of its ability to: (1) predict a second panel's judgments of quality; (2) predict a specific quality-related patient outcome, i.e., compliance with follow-up recommendations; (3) provide a reliable measurement procedure; and (4) detect variations in patterns of emergency department practices. DATA COLLECTION/EXTRACTION METHODS: Data were collected on 2,231 randomly selected emergency psychiatric patients (psychiatric diagnosis, alcohol abuse, nonverbal patients experiencing a psychiatric emergency, and patients with somatic complaints such as life crisis) treated in the emergency departments of the two hospitals. PRINCIPAL FINDINGS: The index predicted physician judgments of quality, was reliable, exhibited sufficient variation in scores, and was strongly associated with patient compliance. CONCLUSIONS: The study demonstrated that a subjective Bayesian model can be used to develop a reliable and valid index for measuring quality of care, with potential for practical application in management of health services.

Bayes Theorem↗

EDECS: the Emergency Department Expert Charting System.

EDECS, the Emergency Department Expert Charting System, integrates clinical guidelines into the everyday practice of medicine. By generating the medical record and patient aftercare instructions, it facilitates patient care. For this reason, doctors are willing to use it. While using it, the doctors are continually presented with advice regarding documentation, testing, and treatment. Unlike guidelines that attempt to modify behavior through traditional educational methods, these computerized guidelines are seen by the physician every time she sees a patient. We have demonstrated this by directly integrating the guidelines into the process of patient care; we can increase compliance with the guidelines [1]. At present EDECS exists for the chief complaints of occupational exposure to body fluids, acute low back pain, recurrent seizure, fever in children, and males with penile discharge or dysuria. Upon examining the patient, the physician proceeds to the computer, which prompts him for essential information regarding the history and physical examination. Certain items are required for all patients with the chief complaint, others are required based on the answers to these items. Data is analyzed by the computer, which provides advice regarding testing and treatment. Once testing is completed, the system suggests a probable diagnosis and aids in patient disposition and discharge planning. Finally, EDECS prints the medical record as well as patient-specific aftercare instructions. EDECS is a user friendly system; most data is entered via mouse. It is written in the OS-based expert system shell AM(TM) and can be run on an IBM compatible PC or PC network. Rules are generally written in an "if...then" format, but more sophisticated rule structures, including Bayesian models, are used when needed. Each module contains separate subroutines for the history, physical, laboratory ordering, treatment, and disposition. These modules call each other in a dynamic fashion. The system is currently being evaluated for its effect on documentation, appropriateness of use of ancillary tests, appropriateness of use of treatments, physician satisfaction, patient satisfaction, and patient outcomes. Initial results of the system's effect on documentation and use of ancillary tests and treatments show much promise [1]. The Occupational Exposure to Body Fluids module has shown a statistically significant, and sometimes rather dramatic, increase in the level of documentation for nearly all items. Further, the advice given by EDECS has caused an increase in the appropriate use of testing and treatments. For example, unnecessary ancillary tests dropped from 1.5 per patient without the computer to 0.1 per patient with the computer's aid. EDECS facilitates quality management activities and research since it collects standardized information and stores it in an easily retrievable database format. It can also be used to educate medical students and residents about the proper care of patients with a given chief complaint. At this session, EDECS will be demonstrated, and issues regarding the development of guidelines, the encoding of guidelines in rules, and the organizational structure of the software will be presented and discussed.

Child↗

Quantitative risk analysis applied to innocuity and potency tests on the oil-adjuvanted vaccine against foot and mouth disease in Argentina.

The authors describe the method used in Argentina for quantification of risk in controls of the potency and innocuity of foot and mouth disease vaccine. Quantitative risk analysis is a relatively new tool in the animal health field, and is in line with the principles of transparency and equivalency of the Sanitary and Phytosanitary Agreement of the Uruguay Round of the General Agreement on Tariffs and Trade (GATT: now World Trade Organisation [WTO]). The risk assessment is presented through a description of the steps involved in manufacturing the vaccine, and the controls performed by the manufacturer and by the National Health Animal Service (Servicio Nacional de Sanidad Animal: SENASA). The adverse situation is considered as the lack of potency or innocuity of the vaccine, and the risk is estimated using a combination of the Monte Carlo simulation and the application of a Bayesian model.

Adjuvants, Immunologic↗

Posterior likelihood methods for multivariate survival data.

This article deals with the semiparametric analysis of multivariate survival data with random block (group) effects. Survival times within the same group are correlated as a consequence of a frailty random block effect. The standard approaches assume either a parametric or a completely unknown baseline hazard function. This paper considers an intermediate solution, that is, a nonparametric function that is reasonably smooth. This is accomplished by a Bayesian model in which the conditional proportional hazards model is used with a correlated prior process for the baseline hazard. The posterior likelihood based on data, as well as the prior process, is similar to the discretized penalized likelihood for the frailty model. The methodology is exemplified with the recurrent kidney infections data of McGilchrist and Aisbett (1991, Biometrics 47, 461-466), in which the times to infections within the same patients are expected to be correlated. The reanalysis of the data has shown that the estimates of the parameters of interest and the associated standard errors depend on the prior knowledge about the smoothness of the baseline hazard.

Bayes Theorem↗

A flexible Bayesian framework for modeling haplotype association with disease, allowing for dominance effects of the underlying causative variants.

Multilocus analysis of single-nucleotide-polymorphism (SNP) haplotypes may provide evidence of association with disease, even when the individual loci themselves do not. Haplotype-based methods are expected to outperform single-SNP analyses because (i) common genetic variation can be structured into haplotypes within blocks of strong linkage disequilibrium and (ii) the functional properties of a protein are determined by the linear sequence of amino acids corresponding to DNA variation on a haplotype. Here, I propose a flexible Bayesian framework for modeling haplotype association with disease in population-based studies of candidate genes or small candidate regions. I employ a Bayesian partition model to describe the correlation between marker-SNP haplotypes and causal variants at the underlying functional polymorphism(s). Under this model, haplotypes are clustered according to their similarity, in terms of marker-SNP allele matches, which is used as a proxy for recent shared ancestry. Haplotypes within a cluster are then assigned the same probability of carrying a causal variant at the functional polymorphism(s). In this way, I can account for the dominance effect of causal variants, here corresponding to any deviation from a multiplicative contribution to disease risk. The results of a detailed simulation study demonstrate that there is minimal cost associated with modeling these dominance effects, with substantial gains in power over haplotype-based methods that do not incorporate clustering and that assume a multiplicative model of disease risks.

Algorithms↗

The application of a Bayesian approach to the analysis of a complex, mechanistically based model.

The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: "informative", "semi-informative", and "noninformative", respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the "informative" priors. However, when the "semi-informative" and "noninformative" priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and "what if" scenarios.

Algorithms↗

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.

Algorithms↗

Fully Bayesian spatio-temporal modeling of FMRI data.

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

Bayes Theorem↗

Application of a semi-dependent latent model in the Bayesian estimation of the sensitivity and specificity of two faecal culture methods for diagnosis of paratuberculosis in sub-clinically infected Greek dairy sheep and goats.

In this study, we compared the frequency of isolation of Mycobacterium avium subsp. paratuberculosis (MAP) from faecal samples grown on Herrold's egg-yolk medium (HEYM) or on Lowenstein-Jensen (LJ) medium and estimated the sensitivity (Se) and specificity (Sp) of the methods separately in sub-clinically infected Greek dairy sheep and goats, using latent-class models and Bayesian estimation procedures. Faecal and blood samples were collected from 400 animals > or =1 year old in April-May 2002. The HEYM supported growth of MAP better than the LJ method and their agreement was very poor (weighted kappa=0.062 (95% CI: -0.098, 0.222)). There was no evidence of dependence between the Ses whereas the Sps were positively correlated. Thus, a semi-dependent model that assumed independence of Ses and accounted for the dependence of Sps was adopted. Under this model, the parallel interpretation of the results of the two methods gave median estimates and 95% credible intervals (CrIs) for Se(par), Sp(par) of 15% (CrIs: 3, 45%), 96% (92, 98%) in sheep and 16% (6, 36%) and 97% (94, 99%) in goats.

Animals↗

Bayesian multivariate hierarchical transformation models for ROC analysis.

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayes Theorem↗

Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.

OBJECTIVE: Until now, the knowledge of combining anesthetics to obtain an adequate level of anesthesia and to economize wakeup time has been empirical and difficult to represent in quantitative models. Since there is no reason to expect that the effect of non-opioid and opioid anesthetics can be modeled in a simple linear manner, the use of a new computational approach with Bayesian belief network software is demonstrated. METHODS: A data set from a pharmacodynamic study was used where remifentanil was randomly given in three fixed target concentrations (2, 4, and 8 ng/ml) to 62 subjects. Target concentrations of propofol were controlled according to the closed-loop system feedback of the auditory evoked potential index to render modeling unbiased by the level of anesthesia. Time to open eyes was measured to represent wakeup time after surgery. The NETICA version 1.37 software was used on a personal computer for network building, validation, and prediction. RESULTS: After the learning phase, the network was used to generate a series of random cases whose probability distribution matches that of the compiled network. The sampling algorithms used are precise, so that the frequencies of the simulated cases will exactly approach the probabilities of the network and that of the data learned. The graphical display of the predicted wakeup time shows less variability but a more complex interaction pattern than with the unadjusted original data. CONCLUSIONS: Model building and evaluation with Bayesian networks does not depend on underlying linear relationships. Bayesian relationships represent true features of the represented data sample. Data may be sparse, uncertain, stochastic, or imprecise. Multiple platform software that is easy to use is increasingly available. Bayesian networks promise to be versatile tools for building valid, nonlinear, predictive instruments to further gain insight into the complex interaction of anesthetics.

Anesthesia Recovery Period↗

A hierarchical Bayesian approach to modeling embryo implantation following in vitro fertilization.

In vitro fertilization and embryo transfer (IVF-ET) is considered a method of last resort for treating infertility. Oocytes taken from a woman are fertilized in vitro, and one or more resulting embryos are transferred into the uterus, with the hope that at least one will implant and result in pregnancy. Successful implantation depends on both embryo viability and uterine receptivity. This has led to the development of the EU model for embryo implantation, wherein uterine receptivity is characterized by a latent binary variable U and embryo viability is characterized by a latent binomial variable E representing the number of viable embryos among those selected for transfer. The observed number of implantations is the product of E and U. Zhou and Weinberg (1998) developed a regression formulation of the EU model in which embryo viabilities are independent within patients. We extend their methodology to a Bayesian hierarchical framework that allows for correlation between the embryo viabilities and gives explicit characterization of patient-level heterogeneity. When some subjects have zero implantations, the likelihood for the hierarchical EU model is relatively flat and therefore using prior information for key parametersis needed. This provides a key motivation for adopting a Bayesian approach. The model is used to assess the effect of hydrosalpinx on embryo implantation in a cohort of 288 women undergoing IVF-ET because of tubal disease. Hydrosalpinx is a build-up of fluid in the Fallopian tubes, which sometimes leaks to the uterus and may reduce the likelihood of implantation. The EU model is well suited to this question because hydrosalpinx is thought to affect implantation by reducing uterine receptivity only. Our analysis indicates substantial subject-level heterogeneity with respect to embryo viability, suggesting the utility of a multi-level model.

Journal Article↗

Bayesian analysis and model selection for interval-censored survival data.

Interval-censored data occur in survival analysis when the survival time of each patient is only known to be within an interval and these censoring intervals differ from patient to patient. For such data, we present some Bayesian discretized semiparametric models, incorporating proportional and nonproportional hazards structures, along with associated statistical analyses and tools for model selection using sampling-based methods. The scope of these methodologies is illustrated through a reanalysis of a breast cancer data set (Finkelstein, 1986, Biometrics 42, 845-854) to test whether the effect of covariate on survival changes over time.

Algorithms↗

Bayesian hierarchical error model for analysis of gene expression data.

MOTIVATION: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY: The software for the HEM is available from the authors upon request.

Algorithms↗

An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation.

Population pharmacokinetic data consists of dose histories, individual covariates and measured drug concentrations with associated sampling times. Population pharmacodynamic data consist of dose histories, covariates and some response measure. Population analyses, whether they be pharmacokinetic or pharmacodynamic attempt to explain the variability observed in the recorded measurements and are increasingly being seen as an important aid in drug development. In this paper a general Bayesian population pharmacokinetic/pharmacodynamic model is described and an analysis of data for the drug recombinant hirudin is presented. The model we use allows for both outliers and censoring in the concentration data and outlying individual pharmacokinetic parameters. We attempt to address directly important questions such as recommended dose size using predictive distributions for response.

Bayes Theorem↗

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo.

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.

Base Sequence↗