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PubMed1989

Optimal use of literature knowledge to improve the Bayesian diagnosis of coronary artery disease.

Bayes' theorem with the independence assumption is applied to a test sample of 141 subjects, using two sets of test sensitivities and specificities. The first set is derived by averaging over literature reports on the accuracy of the exercise electrocardiogram, exercise thallium scintigraphy, and carciac fluoroscopy. The second set of indices is derived by applying multivariate regression to the technical, population, and methodologic attributes obtained from the same literature by the use of meta-analysis. The meta-analytically corrected sensitivities and specificities resulted in significant improvement in the discriminatory power of the Bayes model. (Area under ROC curve increased, p = less than 0.01). However, the corrected model was not as accurate as a data-derived logistic regression model of the same test variables. Meta-analysis may be useful for modest improvement in the accuracy of literature-derived Bayesian models for predicting disease probabilities.

Adult↗
PubMed2005

A Bayesian framework for noise covariance estimation using the facet model.

In image processing literature, thus far, researchers have assumed the perturbation in the data to be white (or uncorrelated) having a covariance matrix sigma2I, i.e., assumption of equal variance for all the data samples and that no correlation exists between the data samples. However, there have been very few attempts to estimate noise characteristics under the assumption that there is a correlation between data samples. In this work, we propose a new and a novel approach for the simultaneous Bayesian estimation of the unknown colored or correlated noise (population) covariance matrix and the hyperparameters of the covariance model using the well-known facet model. We also estimate the facet model coefficients. We use the facet model because of its simple, yet elegant, mathematical formulation. We use the generalized inverted Wishart density as the prior model for the noise covariance matrix. We place a structure on the covariance matrix using the parameters of a correlation filter. These hyperparameters are estimated by a new extension of the expectation-maximization algorithm called the generalized constrained expectation maximization algorithm that we developed.

Algorithms↗
PubMed2004

A Bayesian meta-analysis of the effects of administering an intra-vaginal (CIDR) device in combination with other hormones on the reproductive performance of cycling, anoestrous and inseminated cows.

AIMS: To evaluate the effectiveness of treatment programmes that included controlled internal drug-releasing (CIDR) devices containing progesterone (P4) in improving synchrony of oestrus, and conception and pregnancy rates in cycling, anoestrous and inseminated dairy cows, using meta-analysis. To describe the difference in response between cycling and anoestrous cows to CIDR-based synchrony programmes. METHODS: Scientific papers written in the English language between 1989 and 2002 that investigated the effects of treatment programmes including CIDR devices on reproductive performance in dairy heifers or lactating dairy cows were identified using a computerised literature search. The criteria for inclusion incorporated evidence that treatment allocation was completely randomised; the population studied was lactating dairy cows; and that data were available on submission, conception and pregnancy rates and their associated measures of variability. Reproductive outcomes from 25 synchrony trials (total n=11,058 cows) were analysed. Summary measures of the effect of treatment on reproductive outcome were assessed using fixed- and random-effects Bayesian meta-analysis models. RESULTS: Treatment programmes including a CIDR device increased the risk of submission in cycling cows (predicted Bayesian RR=2.86, 95% credible interval=1.46-5.67). Compared with controls, synchrony programmes including CIDR devices in cycling dairy cows had no effect on the risk of conception to first service post-treatment (predicted Bayesian RR=1.00, 95% credible interval=0.80-1.24). Compared with controls, synchrony programmes including CIDR devices had no effect on the risk of pregnancy throughout the mating period (predicted Bayesian RR=1.02, 95% credible interval=0.89-1.17). In anoestrous cows, CIDR treatment had no effect on the risk of conception to first service post-treatment and no effect on the risk of pregnancy throughout the mating period, compared with anoestrous, untreated controls (predicted Bayesian RR=0.91 and 0.97, respectively; 95% credible interval=0.68-1.26 and 0.59-1.60, respectively). CONCLUSION: The results of this meta-analysis showed that synchrony programmes using CIDR devices combined with other hormones reliably enhanced submission rates in lactating dairy cows. The relatively small number of trials with data suitable for analysis and the heterogeneity of results at the individual trial level limited our ability to confirm either a beneficial or deleterious effect of treatment on conception or pregnancy rates. Further randomised, controlled trials to evaluate the effectiveness of this form of reproductive therapy in commercial dairy farms are needed.

Journal Article↗
PubMed2000

Properties of inductive reasoning.

This paper reviews the main psychological phenomena of inductive reasoning, covering 25 years of experimental and model-based research, in particular addressing four questions. First, what makes a case or event generalizable to other cases? Second, what makes a set of cases generalizable? Third, what makes a property or predicate projectable? Fourth, how do psychological models of induction address these results? The key results in inductive reasoning are outlined, and several recent models, including a new Bayesian account, are evaluated with respect to these results. In addition, future directions for experimental and model-based work are proposed.

Adult↗
PubMed1998

Modelling medical decisions in DynaMoL: a new general framework of dynamic decision analysis.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results indicate practical promise for the framework.

Artificial Intelligence↗
PubMed2005

A Bayesian approach to jointly estimate centre and treatment by centre heterogeneity in a proportional hazards model.

When multicentre clinical trial data are analysed, it has become more and more popular to look for possible heterogeneity in outcome between centres. However, beyond the investigation of such heterogeneity, it is also interesting to consider heterogeneity in treatment effect over centres. For time-to-event outcomes, this may be investigated by including a random centre effect and a random treatment by centre interaction in a Cox proportional hazards model. Assuming independence between the random effects, we propose a Bayesian approach to fit our proposed model. The parameters of interest are the variance components sigma(0) (2) and sigma(1) (2) of these random effects, which can be interpreted as a measure of centre and treatment effect over centres heterogeneity of the hazard. These variance components are estimated from their marginal posterior density after integrating out the fixed treatment effect and the random effects. As this integration cannot be performed analytically, the marginal posterior density is approximated using the Laplace integration technique. Statistical inference is then based on the characteristics of the posterior marginal density, such as the mode and the standard deviation. We demonstrate the proposed technique using data from a pooled database of seven EORTC bladder cancer clinical trials. Substantial centre and treatment effect over centres heterogeneity in disease-free interval was found.

Bayes Theorem↗
PubMed2006

Model for intracellular Lamivudine metabolism in peripheral blood mononuclear cells ex vivo and in human immunodeficiency virus type 1-infected adolescents.

The pharmacologic variability of nucleoside reverse transcriptase inhibitors such as lamivudine (3TC) includes not only systemic pharmacokinetic variability but also interindividual differences in cellular transport and metabolism. A modeling strategy linking laboratory studies of intracellular 3TC disposition with clinical studies in adolescent patients is described. Data from ex vivo laboratory experiments using peripheral blood mononuclear cells (PBMCs) from uninfected human subjects were first used to determine a model and population parameter estimates for 3TC cellular metabolism. Clinical study data from human immunodeficiency virus type 1-infected adolescents were then used in a Bayesian population analysis, together with the prior information from the ex vivo analysis, to develop a population model for 3TC systemic kinetics and cellular kinetics in PBMCs from patients during chronic therapy. The laboratory results demonstrate that the phosphorylation of 3TC is saturable under clinically relevant concentrations, that there is a rapid equilibrium between 3TC monophosphate and diphosphate and between 3TC diphosphate and triphosphate, and that 3TC triphosphate is recycled to 3TC monophosphate through a 3TC metabolite that remains to be definitively characterized. The resulting population model shows substantial interindividual variability in the cellular kinetics of 3TC with population coefficients of variation for model parameters ranging from 47 to 87%. This two-step ex vivo/clinical modeling approach using Bayesian population modeling of 3TC that links laboratory and clinical data has potential application for other drugs whose intracellular pharmacology is a major determinant of activity and/or toxicity.

Adolescent↗
PubMed2005

The cost-effectiveness of basiliximab induction in "old-to-old" kidney transplant programs: Bayesian estimation, simulation, and uncertainty analysis.

INTRODUCTION: Markov models are employed in economic analyses to evaluate all possible expectations in a dilemna. The introduction of a new clinical protocol (Basiliximab induction with calcineurin-sparing protocols) for a group of kidney transplant recipients receiving organs from marginal donors was validated with a Markov simulation model, demonstrating the usefulness of combining simulation with Bayesian estimation methods for analysis of cost-effectiveness data collected alongside a clinical trial. We sought to determine whether calcineurin-sparing protocols using anti-interleukin-2/antibody induction (Simulect) would show a beneficial effect on initial kidney function and reduce transplantation costs upon admission, clinical incidences, graft function, and complications during the first month after transplant. PATIENTS AND METHODS: A Markov Chain Monte Carlo (MCMC) was used to estimate a system of generalized linear models relating costs and outcomes to a kidney transplant process affected by treatment under alternative therapies. The Markov simulation model was established following three chains: a calcineurin-free regimen with Basiliximab induction (chain A); a calcineurin-sparing protocol with Basiliximab induction (chain B); and a conventional immunosuppressive regimen (chain C). The MCMC draws were used as parameters in simulations that yielded inferences about the relative cost-effectiveness of the novel therapy under a variety of scenarios. After designing the Markov chain and cohorts, 31 patients from the "old-to-old" program were assigned; eight to chain A; eight to chain B; and 15 to chain C. A year after transplantation a cost-benefit study was performed guided by the three branches of the Markov model. RESULTS: The Markov model showed a benefit of induction therapies in elderly patients. A cost-benefit model showed that after a year, there was a clear benefit from calcineurin-free plus Basiliximab induction therapies, with a slight benefit from calcineurin-sparing protocols. CONCLUSIONS: Markov models are extremely useful when introducing new clinical therapies. The approach allows flexibility in assessing treatment using various premises and quantifies the global effect of parametric uncertainty on a decision maker's confidence to adopt one therapy over another. In our transplant program, a cost-effective analysis of outcomes in old patients using the Markov model showed a clear benefit of calcineurin-sparing protocols with Basixilimab induction.

Age Factors↗
PubMed2026

Impact of smoking on structural failure after arthroscopic rotator cuff repair: a systematic review and meta-analysis.

BACKGROUND: Rotator cuff tears cause significant shoulder pain and functional limitation. Arthroscopic rotator cuff repair improves symptoms, yet structural failure rates remain substantial. Smoking may impair tendon-to-bone healing, but clinical studies report mixed findings due to heterogeneous methodology. Therefore, a systematic synthesis of imaging-confirmed outcomes is needed to clarify the association between smoking and structural failure after arthroscopic rotator cuff repair. METHODS: This review followed PRISMA 2020 and was registered in PROSPERO (CRD420251246197). PubMed, Embase, Scopus, Web of Science, and the Cochrane Library were searched from inception to 12 December 2025. Comparative clinical studies of adults undergoing arthroscopic rotator cuff repair that reported imaging-confirmed structural integrity (magnetic resonance imaging or ultrasonography) at ≥6 months were included. Two reviewers independently screened studies, extracted data, and assessed quality using the Newcastle-Ottawa Scale. The primary outcome (structural failure) was pooled as risk ratios using a random-effects model with the restricted maximum likelihood estimator and Hartung-Knapp adjustment. Secondary continuous outcomes were synthesized using Bayesian random-effects models; subgroup, sensitivity, and meta-regression analyses explored heterogeneity. RESULTS: Ten cohort studies (1,683 shoulders) were included. Smoking was associated with a higher risk of imaging-confirmed structural failure (risk ratio 1.53; 95% confidence interval 1.13-2.08; P = .011) with low heterogeneity (I2 = 24.7%). Subgroup and sensitivity analyses supported robustness, with no evidence of effect modification by region, follow-up duration, tear size, or smoking definition. Meta-regression showed no significant influence of age, smoking prevalence, or diabetes prevalence on the pooled effect. Secondary outcomes (3 studies) suggested slightly lower postoperative American Shoulder and Elbow Surgeons scores among smokers, while visual analog scale pain scores and forward flexion showed no clear between-group differences. No publication-bias signals were detected for the primary outcome. CONCLUSION: Smoking is associated with a higher risk of imaging-confirmed structural failure after arthroscopic rotator cuff repair. Functional outcomes were broadly similar between groups, with only a small, likely clinically negligible reduction in American Shoulder and Elbow Surgeons scores among smokers. These findings support careful smoking history assessment and perioperative risk modification, including smoking cessation strategies.

Humans↗
PubMed2005

Prediction of protein interdomain linker regions by a hidden Markov model.

MOTIVATION: Our aim was to predict protein interdomain linker regions using sequence alone, without requiring known homology. Identifying linker regions will delineate domain boundaries, and can be used to computationally dissect proteins into domains prior to clustering them into families. We developed a hidden Markov model of linker/non-linker sequence regions using a linker index derived from amino acid propensity. We employed an efficient Bayesian estimation of the model using Markov Chain Monte Carlo, Gibbs sampling in particular, to simulate parameters from the posteriors. Our model recognizes sequence data to be continuous rather than categorical, and generates a probabilistic output. RESULTS: We applied our method to a dataset of protein sequences in which domains and interdomain linkers had been delineated using the Pfam-A database. The prediction results are superior to a simpler method that also uses linker index.

Algorithms↗
PubMed2005

Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes.

In longitudinal studies and in clustered situations often binary and continuous response variables are observed and need to be modeled together. In a recent publication Dunson, Chen, and Harry (2003, Biometrics 59, 521-530) (DCH) propose a Bayesian approach for joint modeling of cluster size and binary and continuous subunit-specific outcomes and illustrate this approach with a developmental toxicity data example. In this note we demonstrate how standard software (PROC NLMIXED in SAS) can be used to obtain maximum likelihood estimates in an alternative parameterization of the model with a single cluster-level factor considered by DCH for that example. We also suggest that a more general model with additional cluster-level random effects provides a better fit to the data set. An apparent discrepancy between the estimates obtained by DCH and the estimates obtained earlier by Catalano and Ryan (1992, Journal of the American Statistical Association 87, 651-658) is also resolved. The issue of bias in inferences concerning the dose effect when cluster size is ignored is discussed. The maximum-likelihood approach considered herein is applicable to general situations with multiple clustered or longitudinally measured outcomes of different type and does not require prior specification and extensive programming.

Animals↗
PubMed1997

Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins.

A model that contains both fixed and random linear regressions is described for analyzing test day records of dairy cows. Estimation of the variances and covariances for this model was achieved by Bayesian methods utilizing the Gibbs sampler to generate samples from the marginal posterior distributions. A single-trait model was applied to yields of milk, fat, and protein of first lactation Holsteins. Heritabilities of 305-d lactation yields were 0.32, 0.28, and 0.28 for milk, fat, and protein, respectively. Heritabilities of daily yields were greater than for 305-d yields and varied from 0.40 to 0.59 for milk yield, 0.34 to 0.68 for fat yield, and 0.33 to 0.69 for protein yield. The highest heritabilities were within the first 10 d of lactation for all traits. Genetic correlations between daily yields were higher as the interval between tests decreased, and correlations of daily yields with 305-d yields were greatest during midlactation.

Algorithms↗
PubMed2003

The effect of sample size and MLP architecture on Bayesian learning for cancer prognosis--a case study.

In this paper we investigate the independent effects of training sample size and multilayer perceptron (MLP) architecture on Bayesian learning to build prognostic models for metastatic breast cancer. We trained two types of Bayesian neural networks on a data set of 1477 metastatic breast cancer patients followed at the Institut Curie using disjoint training sets of sizes k = 50, 100, 200, 300, and 450. The learning performance as measured by an expected loss appeared independent of the two architectures modelling the log hazard function under either proportional or non proportional hazard assumptions, thus indicating that no other sources of nonlinearity besides interactions are present. We found a performance breakdown at k = 50, and no sample size effect for k > or = 100.

Bayes Theorem↗
PubMed2006

Bayesian analysis of interleaved learning and response bias in behavioral experiments.

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

Bayes Theorem↗
PubMed1998

Observer biases in the 3D interpretation of line drawings.

Line drawings produced by contours traced on a surface can produce a vivid impression of the surface shape. The stability of this perception is notable considering that the information provided by the surface contours is quite ambiguous. We have studied the stability of line drawing perception from psychophysical and computational standpoints. For a given family of simple line drawings, human observers could perceive the drawings as depicting either an elliptic (egg-shaped) or hyperbolic (saddle-shaped) smooth surface patch. Rotation of the image along the line of sight and change in aspect ratio of the line drawing could bias the observer toward either interpretation. The results were modeled by a simple Bayesian observer that computes the probability to choose either interpretation given the information in the image and prior preferences. The model's decision rule is noncommitting: for a given input image its responses are still probabilistic, reflecting variability in the modeled observers' judgements. A good fit to the data was obtained when three observer assumptions were introduced: a preference for convex surfaces, a preference for surface contours aligned with the principal lines of curvature, and a preference for a surface orientation consistent with an object viewed from above. We discuss how these assumptions might reflect regularities of the visual world.

Adult↗
PubMed2002

Integrated surface model optimization for freehand three-dimensional echocardiography.

The major obstacle of three-dimensional (3-D) echocardiography is that the ultrasound image quality is too low to reliably detect features locally. Almost all available surface-finding algorithms depend on decent quality boundaries to get satisfactory surface models. We formulate the surface model optimization problem in a Bayesian framework, such that the inference made about a surface model is based on the integration of both the low-level image evidence and the high-level prior shape knowledge through a pixel class prediction mechanism. We model the probability of pixel classes instead of making explicit decisions about them. Therefore, we avoid the unreliable edge detection or image segmentation problem and the pixel correspondence problem. An optimal surface model best explains the observed images such that the posterior probability of the surface model for the observed images is maximized. The pixel feature vector as the image evidence includes several parameters such as the smoothed grayscale value and the minimal second directional derivative. Statistically, we describe the feature vector by the pixel appearance probability model obtained by a nonparametric optimal quantization technique. Qualitatively, we display the imaging plane intersections of the optimized surface models together with those of the ground-truth surfaces reconstructed from manual delineations. Quantitatively, we measure the projection distance error between the optimized and the ground-truth surfaces. In our experiment, we use 20 studies to obtain the probability models offline. The prior shape knowledge is represented by a catalog of 86 left ventricle surface models. In another set of 25 test studies, the average epicardial and endocardial surface projection distance errors are 3.2 +/- 0.85 mm and 2.6 +/- 0.78 mm, respectively.

Algorithms↗
PubMed2004

A nucleotide substitution model with nearest-neighbour interactions.

MOTIVATION: It is well known that neighbouring nucleotides in DNA sequences do not mutate independently of each other. In this paper, we introduce a context-dependent substitution model and derive an algorithm to calculate the likelihood of sequences evolving under this model. We use this algorithm to estimate neighbour-dependent substitution rates, as well as rates for dinucleotide substitutions, using a Bayesian sampling procedure. The model is irreversible, giving an arrow to time, and allowing the position of the root between a pair of sequences to be inferred without using out-groups. RESULTS: We applied the model upon aligned human-mouse non-coding data. Clear neighbour dependencies were observed, including 17-18-fold increased CpG to TpG/CpA rates compared with other substitutions. Root inference positioned the root halfway the mouse and human tips, suggesting an approximately clock-like behaviour of the irreversible part of the substitution process.

Algorithms↗
PubMed2005

Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas.

The present article deals with individual probabilities of different levels of injury in case of a motorcycle accident. The approach uses an empirical Bayesian method based on the Multinomial-Dirichlet model, see [Leonard, T., 1977. A Bayesian approach to some Multinomial estimation and pretesting problems, J. Am. Stat. Association, 72, 869-874], to conduct an analysis of the probability distributions about the severity of accidents at the level of individuals in large and dense French urban areas during year 2003. We model accident severity using four levels of injury: material damages only, slight injury, severe injury, fatal injury. Our application shows that sociodemographic characteristics of motorcyclists and factors influencing their speed behaviors, the suddenness of their collision and the vigilance of road users play significant roles on the shapes of their probability distributions of accident severity. The computation of posterior distributions of the levels of injury for different groups of motorcyclists enables us to rank them with respect to their risk of injury using second order stochastic dominance orderings. It is found that women motorcyclists between 30 and 50 years old driving powerful motorcycles are the most exposed to risk of injury.

Accidents, Traffic↗