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PubMed2026

Exploring hormonal influences on nicotine craving and use across the perinatal period: A prospective longitudinal study.

INTRODUCTION: Perinatal nicotine use is common despite well-documented adverse consequences. We examined associations between reproductive-related hormones with nicotine craving and use during the perinatal period to identify potential novel intervention points. METHODS: All participants reported use of nicotine during the perinatal period. Participants were enrolled at gestational week ≥ 36 and followed to postpartum week 12 via daily surveys (i.e., nicotine craving via 100-point scale, dichotomous use) and weekly hormone measurement in saliva (cortisol, oxytocin) or dried blood spots (progesterone, estradiol, testosterone, dehydroepiandrosterone sulfate). Bayesian mixed-effects models accounted for within-person correlation while estimating hormone effects. RESULTS: Participants (n = 46) were 28.9 ± 4.9 years old. During follow-up, exclusive combustible cigarettes (n = 20), electronic nicotine delivery systems (ENDS; n = 13), or dual (n = 2) use was observed, with variability in use and craving across participants and over time. During pregnancy, higher oxytocin was linked to greater craving (β=16.31, 95% CI: 3.71, 28.83). Greater peripartum declines in oxytocin were associated with more craving (β=8.71, 95% CI: 0.75, 16.93) and use (β=1.13, 95% CI: 0.05, 2.43). During postpartum, lower estradiol was linked to more craving (β=-1.17, 95% CI: -2.15, -0.18) and use (β=-0.40, 95% CI: -0.76, -0.03). In models simultaneously evaluating all postpartum hormones, the lone meaningful association was between estradiol and craving (β=-1.66, 95% CI: -2.84, -0.48). CONCLUSIONS: The results of this study suggest that oxytocin and estradiol may contribute to the risk of perinatal nicotine use. Additional research is needed to replicate our observations in more diverse study samples and explore implications for clinical intervention.

Bayesian↗
PubMed2004

[Access to hospitalization in Brazilian municipalities in 2000: territorial distribution in the Unified National Health System].

This article investigates the effect of external factors on hospitalization patterns in Brazilian municipalities (or counties): supply, spatial configuration, socioeconomic aspects, and political context. Inpatient data from 2000 for individuals 15 years and over and most frequent hospital procedures, obtained from the National Hospital Information System (SIH-SUS), were aggregated by place of residence at the municipal level. Nested generalized additive mixed models were fitted using Bayesian inference. Probability of hospitalization is shown to increase with hospital bed supply and with primary care and local capacity, and to decrease with increasing distances and in larger and wealthier municipalities. Inclusion of random, State, and spatial patterns effects reveals regional differences in the probability of hospitalization and the main factors explaining such different patterns.

Adolescent↗
PubMed2002

National evaluation for calving ease, gestation length and birth weight by linear and threshold model methodologies.

Data included 393,097 calving ease, 129,520 gestation length, and 412,484 birth weight records on 412,484 Gelbvieh cattle. Additionally, pedigrees were available on 72,123 animals. Included in the models were effects of sex and age of dam, treated as fixed, as well as direct, maternal genetic and permanent environmental effects and effects of contemporary group (herd-year-season), treated as random. In all analyses, birth weight and gestation length were treated as continuous traits. Calving ease (CE) was treated either as a continuous trait in a mixed linear model (LM), or as a categorical trait in linear-threshold models (LTM). Solutions in TM obtained by empirical Bayes (TMEB) and Monte Carlo (TMMC) methodologies were compared with those by LM. Due to the computational cost, only 10,000 samples were obtained for TMMC. For calving ease, correlations between LM and TMEB were 0.86 and 0.78 for direct and maternal genetic effects, respectively. The same correlations but between TMEB and TMMC were 1.00 and 0.98, respectively. The correlations between LM and TMMC were 0.85 and 0.75, respectively. The correlations for the linear traits were above.97 between LM and TMEB but as low as 0.91 between LM and TMMC, suggesting insufficient convergence of TMMC. Computing time required was about 2 hrs, 5 hrs, and 6 days for LM, TMEB and TMMC, respectively, and memory requirements were 169, 171, and 445 megabytes, respectively. Bayesian implementation of threshold model is simple, can be extended to multiple categorical traits, and allows easy calculation of accuracies; however, computing time is prohibitively long for large models.

Animals↗
PubMed2003

Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology.

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high-density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.

Biometry↗
PubMed2006

One-shot learning of object categories.

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.

Algorithms↗
PubMed2006

Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.

Algorithms↗
PubMed1993

Evaluating the quality of a probabilistic diagnostic system using different inferencing strategies.

In this paper we describe the evaluation of a probabilistic diagnostic system for patients with renal mass. Three inference models: Multi-membership Bayesian (MB), Minimal Diagnosis (MD) and Bayesian Network (BN), and 72 patients are used to illustrate three interrelated measures of system performance: accuracy, reliability and discriminating power. The inferencing strategies we tested demonstrated the kind of trade-offs in the performance measures that can be expected from imperfect systems. Ultimately, the purpose and expected use of a system should dictate the relative importance ascribed to different aspects of system performance.

Adolescent↗
PubMed2003

Predicting end-stage renal disease: Bayesian perspective of information transfer in the clinical decision-making process at the individual level.

BACKGROUND: Predicting outcomes such as end-stage renal disease (ESRD) by integration and better utilization at individual level of epidemiologic data may facilitate clinical decision-making processes. METHODS: To predict individual ESRD risk in an average patient in the United States, ESRD prevalence and levels of uncertainty and conditional risk factors independence were considered by population data (1998) and pooled analysis of 11 randomized trials. Data integration and input were by decision-tree simulation approach (simple, parallel, and sequential scenarios) and Bayes' theorem. Sensitivity analysis and risk profiles were employed to address uncertainty and assess different risk factor combinations. A health state values, associated with ESRD outcome levels, were taken from the literature. RESULTS: In this theoretical study, we provided a scholarly example about the use of two known risk factors (urinary protein >/=3 g/day and systolic blood pressure >/=140 mm Hg) to predict individual ESRD risk in an average patient in the United States. The highest posterior (decisional) probability of ESRD occurrence (risk of 3.61% to 5.07%) in the individual patient was associated with the worst health state, as assessed by multidimensional scenarios when both risk factors were present. CONCLUSION: Decision tree models through an empirical Bayesian approach may serve to predict the individual ESRD risk on the basis of simple epidemiologic, demographic, and clinical information that is easily available already at the first patient evaluation.

Algorithms↗
PubMed2025

Coupling of spectroscopy and nitrogen-oxygen isotopes unveils the mechanisms of dissolved organic matter and nitrate pollution in lakes within the agro-pastoral transition zone.

Lakes in arid and semi-arid regions are subjected to severe ecological stress, such as organic pollution, eutrophication, and salinization, due to climate change and human activities. This study investigates Chagannur Lake, a typical arid-region lake that is representative and ecologically sensitive in Northern China's agro-pastoral ecotone, to uncover its pollution characteristics and mechanisms. We employed fluorescence spectroscopy and stable isotope analysis to trace dissolved organic matter (DOM) and nitrate sources. The DOM composition was dominated by microbial metabolic byproducts and protein-like substances, suggesting that microbial processes are key to organic matter transformation. Source apportionment revealed that pollutants primarily originated from livestock and poultry manure (37.6 %), agricultural fertilizers (35.6 %), and soil erosion (24.7 %), with agricultural fertilizers contributing most significantly in the Gogstai River (63.3 %). A structural equation model (SEM) coupling spectral and mass spectrometric data revealed that microbial transformation significantly impairs the lake's self-purification capacity, thereby promoting pollutant accumulation (path coefficient = 0.91,*p < 0.05). Moreover, microbial processes link endogenous and exogenous pollution, a mechanism effectively traced by isotopic and fluorescence indices (path coefficient = 0.55, &#x204e;&#x204e;p < 0.01). These findings enhance the understanding of pollution sources and transformation mechanisms in arid-region lakes and offer foundational theoretical support for policymakers engaged in pollution control strategies.

Lakes↗
PubMed1998

Mixed graphical models for simultaneous model identification and control applied to the glucose-insulin metabolism.

In this paper a method for model identification of biological systems described by stochastic linear differential equations using a new computational technique for statistical Bayesian inference, namely mixed graphical models in the sense of Lauritzen and Wermuth, is presented. The model is identified in terms of biological model parameters and noise parameters. This non-linear estimation problem is solved by means of an exact inference algorithm. The parameter estimates are given as a-posteriori distributions which can be interpreted as fuzzy possibility distributions. For model-based simulations of the underlying biological system the model parameters are represented as uncertain parameters with the distributions obtained from the estimation procedure. We apply the presented methods to a model for the glucose-insulin metabolism: the Karlsburg model for type I diabetes.

Bayes Theorem↗
PubMed2005

Nonlinear statistical modeling and model discovery for cardiorespiratory data.

We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.

Algorithms↗
PubMed2002

On the probability model for asthma attacks.

In environmental epidemiology, the impact of environmental agents on symptoms or health status is of interest. This influence is described quantitatively in the theory of Whittemore & Keller (1979). They formulated a logistic model for individuals that is useful in evaluation of panel studies in which each participant protocols whether he does or does not have a certain symptom each day. In the present paper an equation for the prevalence of symptoms in the study population that is defined as the fraction of symptomatic subjects is deduced from the model for individuals. The model for the aggregated quantity depends on the individuals' parameters in a nonlinear manner. The relationship between the individual-based model and the corresponding population-based model is illustrated by means of a simulated panel. Bayesian estimates of the parameters are calculated and compared for both approaches. Bayesian inference enables to apply the prevalence model to a population of non-identical individuals. For such a heterogeneous population, we observe an attenuation of environmental effects on the aggregated symptom prevalence in comparison to the individual-based approach. The presented theory is applicable not only to panel studies but also in time-series analysis of prevalences and incidences.

Asthma↗
PubMed2001

Applications of population approaches in toxicology.

Many experimental or observational studies in toxicology are best analysed in a population framework. Recent examples include investigations of the extent and origin of intra-individual variability in toxicity studies, incorporation of genotypic information to address intra-individual variability, optimal design of experiments, and extension of toxicokinetic modelling to the analysis of biomarker studies. Bayesian statistics provide powerful numerical methods for fitting population models, particularly when complex mechanistic models are involved. Challenges and limitations to the use of population models, in terms of basic structure, computational burden, ease of implementation and data accessibility, are identified and discussed.

Animals↗
PubMed2005

Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle.

Genetic correlations among female fertility traits (linear and binary) were estimated using 225,085 artificial insemination records from 120,713 lactations on 63,160 Holstein cows. Fertility traits were: calving interval, days open, a linear transformation of days open, days to first insemination, interval between first and last insemination, number of inseminations per service period, pregnancy within 56 and 90 d after first insemination, and success in first insemination. A bivariate animal model was implemented using Bayesian methods in the case of binary traits. Low heritabilities (0.02 to 0.06) were estimated for these fertility traits. Strong genetic correlations (0.89 to 0.99) were found among traits, except for days to first service, where the genetic correlation with other fertility traits ranged from -0.52 to -0.18 for binary traits, and from 0.50 to 0.82 for days to first service, calving interval, and days open. Four fertility indices were proposed utilizing information from insemination records; these indices combined one indicator of the beginning of the service period and one indicator of conception rate. Two additional indices used information from the milk-recording scheme, including calving interval and a linear transformation of days open. The fertility index composed of days to first service and pregnancy within 56 d achieved the highest genetic gain for reducing fertility cost, reducing days to first service, and reducing the number of inseminations per lactation ($8.60, -1.31 d, and -0.03 AI, respectively). This index achieved at least 15% higher genetic gain than obtained from indices with information from the milk recording scheme only (calving interval and days open).

Analysis of Variance↗
PubMed2006

Modeling a syphilis outbreak through space and time using the Bayesian maximum entropy approach.

PURPOSE: The aim of the study is to describe changes in the spatial distribution of syphilis before, during, and after an outbreak in Baltimore, MD, by using Bayesian maximum entropy (BME), a modern geostatistical technique for space-time analysis and mapping. METHODS: BME was used to conduct simple and composite space-time analyses of the density of syphilis infection based on primary, secondary and early latent syphilis cases reported to the Baltimore City Health Department between January 1, 1994, and December 31, 2002. RESULTS: Spatiotemporal covariance plots indicated that the distribution of the density of syphilis cases showed both spatial and temporal dependence. Temporally dependent disease maps suggested that syphilis increased within two geographic core areas of infection and spread outward. A new core area of infection was established to the northwest. As the outbreak waned, density diminished and receded in all core areas. Morbidity remained elevated in the two original central and new northwestern core areas after the outbreak. CONCLUSIONS: Density of syphilis infection was a simple informative measure easily compared across years. The BME approach was useful for quantitatively and qualitatively describing the spatial development and spread of syphilis. Our results are specific to Baltimore; however, the BME approach is generalizable to other settings and diseases.

Baltimore↗
PubMed1995

Bayesian forecasting of serum vancomycin concentrations in neonates and infants.

A dynamic pharmacokinetic model for i.v. vancomycin administration was developed and tested in 47 neonates and infants. Twenty-nine patients (Group 1), having two or more concentrations, were used to estimate population parameters by nonlinear least-squares analysis. Multiple stepwise linear regression techniques showed that estimated creatinine clearance, Clcr, and postnatal age were significant demographic factors related to vancomycin clearance (CL). No strong associations were found for the apparent volume of distribution. A one-compartment model was constructed using the associations of CLcr and postnatal age with vancomycin CL. Eighteen patients (Group 2), receiving 35 courses of vancomycin therapy, with both initial and subsequent sets of peak and trough concentrations, were used to test the predictive performance of the model with and without the use of Bayesian forecasting. Using only population-based parameters, the respective mean error (ME) (bias) and mean absolute error (MAE) (precision) for predicting subsequent peak concentrations were -1.20 and 3.89 mg/L and for trough concentrations, 0.83 and 2.23 mg/L, respectively. For the Bayesian method, these values were, respectively, 0.45 and 4.13 mg/L for peak concentrations and 1.55 and 2.40 mg/L for trough concentrations. When predicted concentrations occurred within 30 days of feedback concentrations, the Bayesian method tended to be slightly less biased and more precise than the population-based parameters. The opposite was true > 30 days of the initial set of feedback concentrations. The use of population-specific pharmacokinetic parameters and Bayesian forecasting should allow accurate dosage regimen design as well as minimize the need for monitoring serum vancomycin concentrations in neonates and young infants.

Bayes Theorem↗
PubMed2006

A simulation study of three methods for detecting disease clusters.

BACKGROUND: Cluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized additive models (GAM) and (3) Bayesian disease mapping (BYM). We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster. RESULTS: The performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters. CONCLUSION: Based on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation) to get satisfactory results.

Bayes Theorem↗
PubMed1996

A technique for measuring epidemiologically useful features of birthweight distributions.

Birthweight distributions have been conceptualized as a predominant Gaussian distribution contaminated in the tails by an unspecified 'residual' distribution. Acknowledging this idea, we propose a technique for measuring certain features of birthweight distributions useful to epidemiologists: the mean and variance of the predominant distribution; the proportions of births in the low- and high-birthweight residual distributions, and the boundaries of support for these residual distributions. Our technique, based on an underlying multinomial sampling distribution, involves estimating parameters in a mixture model for the multinomial bin probabilities after having chosen the support of the residual distribution with a model selection criterion. A modest simulation study and experience with a few actual datasets indicate that use of a Bayesian information criterion (BIC) as model selection criterion is superior to use of Akaike's information criterion (AIC) in this application.

Analysis of Variance↗