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PubMed1997

Crash reductions related to traffic signal removal in Philadelphia.

The effect on intersection crashes of converting one-way street intersections in Philadelphia from signal to multiway stop sign control was estimated. Using crash and traffic volume data for a comparison group, regression models were computed to represent the normal crash experience of signal controlled intersections of one-way streets, by impact type, as a function of traffic volume. An empirical Bayesian procedure was used to estimate what would have been the expected number of crashes at the converted intersections had they not been converted. The empirical Bayesian estimates were compared with actual counts of crashes after conversion. Estimates were obtained for different classes of crashes categorized by impact type, day/night condition, and impact severity. Aggregate results indicate that replacing signals by multiway stop signs on one-way streets is associated with a reduction in crashes of approximately 24%, combining all severities, light conditions, and impact types.

Accidents, Traffic↗
PubMed1989

X linked or autosomal recessive? A new approach to an old problem.

Families in which a single male is affected with a disease which might be either X linked recessive or autosomal recessive present problems in counselling. Before female relatives can be counselled, the probabilities of each mode of inheritance must be assessed, taking into account the prior probabilities, the pedigree structure, any DNA probe data, and any carrier testing data. The widely used linkage analysis package LINKAGE can be used to do the calculation, which is much simpler than the conventional Bayesian method.

Bayes Theorem↗
PubMed1997

A population pharmacokinetic model of trimethoprim in patients with pneumocystis pneumonia, made with parametric and nonparametric methods.

A population pharmacokinetic model of intravenously and orally administered trimethoprim in patients with acquired immunodeficiency syndrome and Pneumocystis carinii pneumonia has been made using a parametric iterative two-stage Bayesian and a nonparametric expectation maximization computer program. When good information was present in the serum level data, both methods obtained similar results. With the nonparametric expectation maximization program, the median apparent rate constant for absorption (Ka) was 1.602 hr-1, median slope (Ks) of the relationship between creatinine clearance and elimination was 0.001168 hr-1, median apparent volume of distribution (Vs) was 1.058 l/kg, and median fraction of oral dose absorbed (Fa) was 0.955. These results permit dosage individualization adjusted to body weight and renal function to achieve chosen serum level peak and trough goals. Peak goals of 9 ug/ml and trough goals of 5 ug/ml appear reasonable for most patients in this population, and should permit most to complete an effective course of therapy with a reduced risk for treatment-terminating hematologic toxicity. However, therapeutic goals should always be selected based on each patient's apparent need for the drug and the risk of toxicity that is justifiably acceptable to obtain the expected benefits of the drug.

AIDS-Related Opportunistic Infections↗
PubMed1994

Statistical models for PET and SPECT data.

This article outlines the statistical developments that have taken place in emission tomography during the past decade or so. We discuss the statistical aspects of the modelling of the projection data and define the additive Poisson regression model. This leads to the use of the method of maximum likelihood as a means of estimating the underlying isotope concentration within a given region of a patient's body, and to the use of the EM algorithm to compute the reconstruction. The need for the regulation of the maximum likelihood solution is tackled using Bayesian techniques. A number of algorithms for the computation of regularized solutions are outlined. The issue of parameter estimation is discussed and some open issues are mentioned.

Algorithms↗
PubMed1997

Automated analysis of the American College of Radiology mammographic accreditation phantom images.

A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology (ACR) mammographic accreditation phantom (MAP) images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles (five of which were specially trained in mammography) viewed the original film images under similar conditions and provided individual scores for each test object (fibrils, microcalcifications, and nodules). Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast threshold established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils (agreement occurred for the other test objects). The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed.

Accreditation↗
PubMed1981

The role of modeling methods in medical diagnosis.

Modeling methods in medical diagnosis are concerned with medical information processing as it pertains to utilizing biological modeling methods to facilitate patient care. Major considerations in this particular area are (1) the classification problem related to the establishment of disease entities-the taxonomy problem, and (2) the diagnosis of diseases. Available are properties, criteria, signs, symptoms, and manifestations of diseases that have been cumulated and categorized by clinicians and researchers. The problem is to optimally utilize the information content of a sign or set of signs in the practice of patient care as pertaining to the medical diagnosis problem. Some mathematical approaches implemented to facilitate such analyses include cluster analysis, discriminant analysis, Bayesian methods, computer approaches, game theory, information theory, stochastic representations, stepwise procedures, decision analysis, and pattern recognition techniques. Each of these has been studied in depth by numerous researchers advocating computer applications in medicine. Here we discuss the scope and limitations of utilizing modeling methods as a viable approach to interpreting vast amounts of biological data collected on a single patient during an encounter. We consider the following: (1) limitations associated with modeling methodologies; (2) levels of responsibilities, ranging over logging, summarizing, reporting, monitoring, and therapy selection; and (3) operational strategies and considerations as they affect hardware logistics, the actual algorithm utilized, and implementation of these sophisticated analysis systems.

Decision Theory↗
PubMed1997

Constructing probabilistic models.

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas.

Algorithms↗
PubMed2026

Comparing ARG Inference Methods Under Transmission of Reproductive Success: Tree Imbalance Matters.

Inferring coalescent trees from genomic data has become a major subject in population genetics, particularly with the recent advances in tree sequence reconstruction methods. However, it remains unclear how well these methods perform for imbalanced genealogies. Such imbalances can arise from processes such as cultural transmission of reproductive success (CTRS) or positive selection. Using simulated genomic data, we benchmarked three major software packages, SINGER, Relate, and tsinfer, by comparing the imbalance of reconstructed trees by these methods with that of the true simulated trees, for three indices that quantify this imbalance. The three methods performed well under scenarios yielding balanced trees. However, their accuracy declined as imbalance increased. Performances also varied with mutation rate, recombination rate, and sample size. This study opens possibilities for applying these methods to infer CTRS or positive selection in large-scale genomic datasets, using simulation-based inference such as approximate Bayesian computation.

Models, Genetic↗
PubMed1997

Efficient temporal probabilistic reasoning via context-sensitive model construction.

We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a sound and complete algorithm for computing posterior probabilities of temporal queries, as well as an efficient implementation of the algorithm. Throughout we illustrate the approach with the problem of reasoning about the effects of medications and interventions on the state of a patient in cardiac arrest. We empirically evaluate the efficiency of our system by comparing its inference times on problems in this domain with those of standard Bayesian network representations of the problems.

Algorithms↗
PubMed1997

Advances in statistical methods to map quantitative trait loci in outbred populations.

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.

Bayes Theorem↗
PubMed1994

A bayesian approach to Arrhenius prediction of shelf-life.

The use of a bayesian method for estimating the shelf-life of pharmaceutical formulations is evaluated and compared with classical linear regression. Using three real data sets, the greater flexibility provided by the bayesian approach is demonstrated. In particular, the bayesian approach enabled the consideration of cases when the error distribution is non-normal. However much more computation is required with the bayesian method.

Bayes Theorem↗
PubMed1991

Using empirical Bayes methods in biopharmaceutical research.

A compound sampling model, where a unit-specific parameter is sampled from a prior distribution and then observed are generated by a sampling distribution depending on the parameter, underlies a wide variety of biopharmaceutical data. For example, in a multi-centre clinical trial the true treatment effect varies from centre to centre. Observed treatment effects deviate from these true effects through sampling variation. Knowledge of the prior distribution allows use of Bayesian analysis to compute the posterior distribution of clinic-specific treatment effects (frequently summarized by the posterior mean and variance). More commonly, with the prior not completely specified, observed data can be used to estimate the prior and use it to produce the posterior distribution: an empirical Bayes (or variance component) analysis. In the empirical Bayes model the estimated prior mean gives the typical treatment effect and the estimated prior standard deviation indicates the heterogeneity of treatment effects. In both the Bayes and empirical Bayes approaches, estimated clinic effects are shrunken towards a common value from estimates based on single clinics. This shrinkage produces more efficient estimates. In addition, the compound model helps structure approaches to ranking and selection, provides adjustments for multiplicity, allows estimation of the histogram of clinic-specific effects, and structures incorporation of external information. This paper outlines the empirical Bayes approach. Coverage will include development and comparison of approaches based on parametric priors (for example, a Gaussian prior with unknown mean and variance) and non-parametric priors, discussion of the importance of accounting for uncertainty in the estimated prior, comparison of the output and interpretation of fixed and random effects approaches to estimating population values, estimating histograms, and identification of key considerations in the use and interpretation of empirical Bayes methods.

Bayes Theorem↗
PubMed1995

Estimation of population pharmacokinetics using the Gibbs sampler.

Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.

Anti-Bacterial Agents↗
PubMed1998

Biases in three-dimensional structure-from-motion arise from noise in the early visual system.

The projected pattern of retinal-image motion supplies the human visual system with valuable information about properties of the three-dimensional environment. How well three-dimensional properties can be recovered depends both on the accuracy with which the early motion system estimates retinal motion, and on the way later processes interpret this retinal motion. Here we combine both early and late stages of the computational process to account for the hitherto puzzling phenomenon of systematic biases in three-dimensional shape perception. We present data showing how the perceived depth of a hinged plane ('an open book') can be systematically biased by the extent over which it rotates. We then present a Bayesian model that combines early measurement noise with geometric reconstruction of the three-dimensional scene. Although this model has no in-built bias towards particular three-dimensional shapes, it accounts for the data well. Our analysis suggests that the biases stem largely from the geometric constraints imposed on what three-dimensional scenes are compatible with the (noisy) early motion measurements. Given these findings, we suggest that the visual system may act as an optimal estimator of three-dimensional structure-from-motion.

Computer Simulation↗
PubMed1994

Design of dosage regimens: a multiple model stochastic control approach.

This paper presents a general stochastic control framework for determining drug dosage regimens where the sample times, dosing times, desired goals, etc., occur at different times and in an asynchronous fashion. In the special case of multiple models with linear dynamics and quadratic cost (MMLQ), it is shown that the optimal open-loop stochastic control with linear control/state constraints can be solved exactly and efficiently as a quadratic program. This provides a simple and flexible method for computing open-loop feedback designs of drug dosage regimens. An implementation of the MMLQ adaptive control approach is demonstrated on a Lidocaine infusion process. For this example, the resulting MMLQ regimen is more effective than the MAP Bayesian regimen at reducing interpatient variability and keeping patients in the therapeutic range.

Drug Administration Schedule↗
PubMedDate not supplied

Bayesian analysis of ROC curves using Markov-chain Monte Carlo methods.

The authors introduce a Bayesian approach to generalized linear regression models for rating data observed in the evaluation of a diagnostic technology. Such models were previously studied using a non-Bayesian approach. In a Bayesian analysis, the difficulties inherent in an ordinal rating scale are circumvented by using data-augmentation techniques. Posterior distributions for the regression parameters- and thereby for receiver operating characteristic (ROC) curve parameters and values, for the area under a ROC curve, differences between areas, etc.-may then be computed by Markov-chain Monte Carlo methods. Inferences are made in standard Bayesian ways. The methods are exemplified by a study of ultrasonography rating data for the detection of hepatic metastases in patients with colon or breast cancer (previously analyzed) and the results compared.

Bayes Theorem↗
PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗
PubMed1993

Computer dosing program for the initiation of vancomycin therapy.

The predictive performance of a computer dosing program used for initiating vancomycin therapy was studied. Initial serum vancomycin concentrations in 31 adult patients receiving vancomycin were estimated by using a computer program (T.D.M.S.) incorporating a two-compartment open model. Sixty-two serum vancomycin concentrations at steady state (Css) were obtained before and after one-hour infusions and compared with estimated Css values. Bias and precision were evaluated by calculating median error (ME) and median absolute error (MAE), respectively. Population-based estimates of volume of distribution (V) and clearance (CL) were compared with those obtained by fitting each patient's data set by using Bayesian analysis (BA) and non-linear least-squares regression (NLLS). Median (mean +/- S.D.) bias and precision for peak Css were 7.7 (10.2 +/- 10.8) and 7.7 (10.6 +/- 10.5) mg/L, and for trough Css were 7.4 (7.7 +/- 7.6) and 7.4 (8.8 +/- 6.2) mg/L. The medians were significantly different from zero. Estimated median (mean +/- S.D.) V, CL, and half-life were 0.72 L/kg, 0.60 (0.67 +/- 0.21) mL/min/kg, and 11.59 (12.87 +/- 3.91) hours. Median (mean +/- S.D.) CL values determined by BA and NLLS were 0.86 (0.89 +/- 0.32) and 0.85 (0.92 +/- 0.34) mL/min/kg, respectively. Both CL values were significantly greater than the population-based estimate. However, median V values determined by BA and NLLS did not differ from the population-based estimate. A revised clearance model derived from Bayesian analysis of data for the first 21 patients was tested in the 10 other patients and appeared to improve the predictive performance of the a priori model.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗