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[On the importance of the "decision-making model" view of diagnosis as a clinical framework in psychiatry].

After the advent of DSM-III, operational diagnostic criteria, along with the classification of disorders using such criteria, received considerable attention, and many studies on the reliability and validity of psychiatric diagnosis were conducted worldwide. Operational methodology was applied to diagnosis and classification, especially, in the area of research, and has contributed greatly to advances in reliable and refined clinical research. Such methodology, however, has not necessarily been accepted as a guiding principle in the area of clinical practice by all psychiatrists. Rather, some psychiatrists, especially more experienced psychiatrists, took a somewhat negative attitude toward the use of operational methodology. The author contends that one of the causes for the relatively poor acceptance of operational methodology in the area of clinical practice lies in the "classification model" view of diagnosis that forms the implicit background for the methodology. From a clinical perspective, it is not from the "classification model" basis but rather, from the "decision-making model" basis that the actual process of clinical diagnosis in psychiatry is explained properly. This is a very important point, because the latter model is potentially more useful both to psychiatric patients and to researchers in psychiatry than the former model. There have been however, few reports in psychiatry that highlight the importance of this model as the clinical framework. The author analyzes the limitations of the "classification model" view, and then, based on this analysis, lists prerequisites that a model for the framework of clinical practice should possess. The prerequisites listed are: that clinical information not sufficient to meet the disease criteria should be used as effectively as possible, that diseases low in probability but high in seriousness should be considered by clinicians in the differential diagnoses; that diagnosis should be readily changed when necessary; that the component of benefit to the patient should be included in the model; and that the relationship between nosological definition of diseases and practical diagnostic criteria should be explained explicitly. In addition, the author emphasizes that psychiatric diagnosis involves "decision-making under the condition of incomplete information", because most psychiatric diseases are still of unknown etiology. With all these conditions in mind, the author contends that the Bayesian statistical model of decision-making is suited to the frame of reference, and that it should replace the classification model as a guiding principle. This model can integrate various aspects of psychiatric clinical activities systematically and explicitly. A case is presented to illustrate the model in a clinical context. The author points out problems to be solved in current psychiatry from the perspective of the Bayesian model of decision-making. Finally, limitations of the Bayesian model view are discussed.

Bayes Theorem↗

Bayesian spatial and ecological models for small-area accident and injury analysis.

In this article, recently developed Bayesian spatial and ecological regression models are applied to analyse small-area variation in accident and injury. This study serves to demonstrate how Bayesian modelling techniques can be implemented to assess potential risk factors measured at group (e.g. area) level. Presented here is a unified modelling framework that enables thorough investigations into associations between injury rates and regional characteristics, residual variation and spatial autocorrelation. Using hospital separation data for 83 local health areas in British Columbia (BC), Canada, in 1990-1999, we explore and examine ecological/contextual determinants of motor vehicle accident injury (MVAI) among male children and youth aged 0-24 and for those of six age groups (<1, 1-4, 5-9, 10-14, 15-19 and 20-24). Eighteen local health area characteristics are studied. They include a broad spectrum of socio-economic indicators, residential environment indicators (roads and parks), medical services availability and utilisation, population health, proportion of recent immigrants, crime rates, rates of speeding charge and rates of seatbelt violation. Our study indicates a large regional variation in MVAI in males aged 0-24 in British Columbia, Canada, in 1990-1999, and that adjusting for appropriate risk factors eliminates nearly all the variation observed. Socio-economic influence on MVAI was profoundly apparent in young males of all ages with the injury being more common in communities of lower socio-economic status. High adult male crime rates were significantly associated with high injury rates of boys aged 1-14. Seatbelt violations and excess speeding charges were found to be positively associated with the injury rates of young men aged 20-24. This and similar ecological studies shed light on reasons for regional variations in accident occurrence as well as in the resulting injuries and hospital utilisation. Thereby they are potentially useful in identifying priority areas for injury/accident prevention and in informing regional health planning and policy development.

Accidents↗

Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds.

Experimental programs have been underway for several years to determine the environmental effects of chemical compounds, mixtures, and the like. Among these programs is the National Toxicology Program (NTP) on rodent carcinogenicity. Because these experiments are costly and time-consuming, the rate at which test articles (i.e., chemicals) can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. To assist human experts in organizing an empirical testing regime, and to try to shed light on mechanisms of toxicity, we constructed toxicity models using various machine learning and data mining methods, both existing and those of our own devising. These models took the form of decision trees, rule sets, neural networks, rules extracted from trained neural networks, and Bayesian classifiers. As a training set, we used recent results from rodent carcinogenicity bioassays conducted by the NTP on 226 test articles. We performed 10-way cross-validation on each of our models to approximate their expected error rates on unseen data. The data set consists of physical-chemical parameters of test articles, alerting chemical substructures, salmonella mutagenicity assay results, subchronic histopathology data, and information on route, strain, and sex/species for 744 individual experiments. These results contribute to the ongoing process of evaluating and interpreting the data collected from chemical toxicity studies.

Animals↗

Efficient sampling strategies for forecasting pharmacokinetic parameters of irinotecan (CPT-11): implication for area under the concentration-time curve monitoring.

A linear two-compartment Bayesian pharmacokinetic model was developed using a standard two-stage population method for the novel anti-cancer agent CPT-11 from 11 adult patients with refractory cancer. The accuracy and efficiency of this Bayesian model for estimating pharmacokinetic parameters including the area under the concentration-time curve (AUC) was then evaluated using two different sampling strategies in a new study cohort of 13 patients with cancer. Sampling strategies included either one, two, or three nonsteady-state feedback levels determined empirically and from optimal sampling theory (D-optimality). All 24 patients in this study received CPT-11 (60 mg/m2) as a 90-min infusion. Pharmacokinetic parameters derived from the Bayesian model combined with these limited sampling strategies were compared with those parameters obtained from the full sample data sets (n = 10) analyzed by weighted nonlinear least squares regression (reference method). The least-bias and most precise sampling times for estimating AUC were 3.5; 3.5 and 9.5; and 0.5, 3.5, and 9.5 h, respectively. At these times, only marginal improvement in precision of the AUC estimate was observed using two versus three samples. However, the precision of the estimate of clearance was not improved using two versus three samples. The sampling times derived from optimal sampling theory were 0.25, 3.5, 8.5, and 24 h and correlated closely to the actual and best empirical sampling times of 0.5, 3.5, 9.5, and 24 h. These results strongly suggest that Bayesian estimation combined with only two optimally timed samples accurately predicts the AUC of CPT-11 and should be useful for implementing adaptive control dosing for monitoring CPT-11 systemic exposure in patients with cancer.

Adult↗

An informative Bayesian structural equation model to assess source-specific health effects of air pollution.

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.

Air Pollution↗

Minimal model SG overestimation and SI underestimation: improved accuracy by a Bayesian two-compartment model.

The intravenous glucose tolerance test (IVGTT) single-compartment minimal model (1CMM) method has recently been shown to overestimate glucose effectiveness and underestimate insulin sensitivity. Undermodeling, i.e., use of single- instead of two-compartment description of glucose kinetics, has been advocated to explain these limitations. We describe a new two-compartment minimal model (2CMM) into which we incorporate certain available knowledge on glucose kinetics. 2CMM is numerically identified using a Bayesian approach. Twenty-two standard IVGTT (0.30 g/kg) in normal humans were analyzed. In six subjects, the clamp-based index of insulin sensitivity (ScI) was also measured. 2CMM glucose effectiveness (S2G) and insulin sensitivity (S2I) were, respectively, 60% lower (P < 0.0001) and 35% higher (P < 0.0001) than the corresponding 1CMM S1G and S1I indexes: 2.81 +/- 0.29 (SE) vs. S1G = 4.27 +/- 0.33 ml. min(-1). kg(-1) and S2I = 11.67 +/- 1.71 vs. S1I = 8.68 +/- 1.62 10(2) ml. min(-1). kg(-1) per microU/ml. S2I was not different from ScI = 12.61 +/- 2.13 10(2) ml. min(-1). kg(-1) per microU/ml (nonsignificant), whereas S1I was 60% lower (P < 0.02). In conclusion, a new 2CMM has been presented that improves the accuracy of glucose effectiveness and insulin sensitivity estimates of the classic 1CMM from a standard IVGTT in normal humans.

Adult↗

Bayesian analysis for erosion modelling of sediments in combined sewer systems.

Previous research has confirmed that the sediments at the bed of combined sewer systems are the main source of particulate and organic pollution during rain events contributing to combined sewer overflows. However, existing urban stormwater models utilize inappropriate sediment transport formulas initially developed from alluvial hydrodynamics. Recently, a model has been formulated and profoundly assessed based on laboratory experiments to simulate the erosion of sediments in sewer pipes taking into account the increase in strength with depth in the weak layer of deposits. In order to objectively evaluate this model, this paper presents a Bayesian analysis of the model using field data collected in sewer pipes in Paris under known hydraulic conditions. The test has been performed using a MCMC sampling method for calibration and uncertainty assessment. Results demonstrate the capacity of the model to reproduce erosion as a direct response to the increase in bed shear stress. This is due to the model description of the erosional strength in the deposits and to the shape of the measured bed shear stress. However, large uncertainties in some of the model parameters suggest that the model could be over-parameterised and necessitates a large amount of informative data for its calibration.

Bayes Theorem↗

Bayesian proportional hazards model with time-varying regression coefficients: a penalized Poisson regression approach.

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coefficients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these coefficients. We show how the regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm.

Bayes Theorem↗

MrBayes 3: Bayesian phylogenetic inference under mixed models.

MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.

Algorithms↗

Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays.

BACKGROUND: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences. RESULTS: New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic for the power to detect small differences in gene expression. CONCLUSIONS: Understanding the resolution of difference in gene expression that is detectable as significant is a vital component of experimental design and evaluation. These small differences in gene expression level are readily detected with a Bayesian analysis of gene expression level that has additive error terms and constrains samples to have a common error coefficient of variation. The power to detect small differences in a study may then be determined by logistic regression.

Algorithms↗

Bayesian nonparametric population models: formulation and comparison with likelihood approaches.

Population approaches to modeling pharmacokinetic and/or pharmacodynamic data attempt to separate the variability in observed data into within- and between-individual components. This is most naturally achieved via a multistage model. At the first stage of the model the data of a particular individual is modeled with each individual having his own set of parameters. At the second stage these individual parameters are assumed to have arisen from some unknown population distribution which we shall denote F. The importance of the choice of second stage distribution has led to a number of flexible approaches to the modeling of F. A nonparametric maximum likelihood estimate of F was suggested by Mallet whereas Davidian and Gallant proposed a semiparametric maximum likelihood approach where the maximum likelihood estimate is obtained over a smooth class of distributions. Previous Bayesian work has concentrated largely on F being assigned to a parametric family, typically the normal or Student's t. We describe a Bayesian nonparametric approach using the Dirichlet process. We use Markov chain Monte Carlo simulation to implement the procedure. We discuss each procedure and compare our approach with those of Mallet and Davidian and Gallant, using simulated data for a pharmacodynamic dose-response model.

Bayes Theorem↗

Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests.

Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.

Bayes Theorem↗

Bayesian latent variable models for mixed discrete outcomes.

In studies of complex health conditions, mixtures of discrete outcomes (event time, count, binary, ordered categorical) are commonly collected. For example, studies of skin tumorigenesis record latency time prior to the first tumor, increases in the number of tumors at each week, and the occurrence of internal tumors at the time of death. Motivated by this application, we propose a general underlying Poisson variable framework for mixed discrete outcomes, accommodating dependency through an additive gamma frailty model for the Poisson means. The model has log-linear, complementary log-log, and proportional hazards forms for count, binary and discrete event time outcomes, respectively. Simple closed form expressions can be derived for the marginal expectations, variances, and correlations. Following a Bayesian approach to inference, conditionally-conjugate prior distributions are chosen that facilitate posterior computation via an MCMC algorithm. The methods are illustrated using data from a Tg.AC mouse bioassay study.

Acrylates↗

A Bayesian approach to modelling the natural history of a chronic condition from observations with intervention.

To assess the costs and benefits of screening and treatment strategies, it is important to know what would have happened had there been no intervention. In today's ethical climate, however, it is almost impossible to observe this directly and therefore must be inferred from observations with intervention. In this paper, we illustrate a Bayesian approach to this situation when the observations are at separated and unequally spaced time points and the time of intervention is interval censored. We develop a discrete-time Markov model which combines a non-homogeneous Markov chain, used to model the natural progression, with mechanisms that describe the possibility of both treatment intervention and death. We apply this approach to a subpopulation of the Wisconsin Epidemiologic Study of Diabetic Retinopathy, a population-based cohort study to investigate prevalence, incidence, and progression of diabetic retinopathy. In addition, posterior predictive distributions are discussed as a prognostic tool to assist researchers in evaluating costs and benefits of treatment protocols. While we focus this approach on diabetic retinopathy cohort data, we believe this methodology can have wide application.

Age of Onset↗

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗

Potts model for haplotype associations.

Bayesian spatial modeling has become important in disease mapping and has also been suggested as a useful tool in genetic fine mapping. We have implemented the Potts model and applied it to the Genetic Analysis Workshop 14 (GAW14) simulated data. Because the "answers" were known we have analyzed latent phenotype P1-related observed phenotypes affection status (genetically determined) and i (random) in the Danacaa population replicate 2. Analysis of the microsatellite/single-nucleotide polymorphism-based haplotypes at chromosomes 1 and 3 failed to identify multiple clusters of haplotype effects. However, the analysis of separately simulated data with postulated differences in the effects of the two clusters has yielded clear estimated division into the two clusters, demonstrating the correctness of the algorithm. Although we could not clearly identify the disease-related and the non-associated groups of haplotypes, results of both GAW14 and our own simulation encourage us to improve the efficiency and sensitivity of the estimation algorithm and to further compare the proposed method with more traditional methods.

Computer Simulation↗

A model-integrated multipoint Bayesian analysis of hypertension in the Framingham Heart Study data finds little evidence of linkage.

This Genetic Analysis Workshop 13 contribution presents a linkage analysis of hypertension in the Framingham data based on the posterior probability of linkage, or PPL. We dichotomized the phenotype, coding individuals who had been treated for hypertension at any time, as well as those with repeated high blood pressure measurements, as affected. Here we use a new variation on the multipoint PPL that incorporates integration over the genetic model. PPLs were computed for chromosomes 1 through 5, 11, 14, and 17 and remained below the 2% assumed prior probability of linkage for 73% of the locations examined. The maximum PPL of 4.5% was obtained on chromosome 1 at 178 cM. Although this is more than twice the assumed prior probability of linkage, it is well below a level at which we would recommend committing substantial additional resources to molecular follow-up. While the PPL analysis of this data remains inconclusive, Bayesian methodology gives us a clear mechanism for using the information gained here in further studies.

Bayes Theorem↗

Safety and efficacy of a two-compartment Bayesian feedback program for therapeutic tobramycin monitoring in the daily clinical use and comparison with a non-Bayesian one-compartment model.

The predictive value of a two-compartment Bayesian feedback program for tobramycin dose optimization was retrospectively evaluated in 199 hospitalized patients and compared with that of a simple non-Bayesian one-compartment model. Before dose adjustment, 64% of the patients were underdosed indicating that tobramycin monitoring is still necessary to avoid ineffective antibiotic therapy. When physicians adhered to the dose instructions calculated with the Bayesian method, 90% of the patients had optimal concentration-time profiles as opposed to only 53% of the 43 patients in whom dose recommendations were not followed. In young patients with normal renal function, precision and accuracy of the Bayesian feedback and the one-compartment method were well correlated, whereas in elderly patients (> 60 years) and patients with impaired renal function (estimated creatinine clearance < 60 ml/minute), the Bayesian method was significantly more precise. Multiple regression analysis revealed that renal function was the only independent variable predicting the performance of the Bayesian program. The results of this study indicate that the Bayesian feedback method is a reliable method for the therapeutic tobramycin monitoring under clinical conditions and in particular, elderly patients in whom renal impairment is frequent.

Absorption↗