An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Attachment among Adoptive Families: Two Emipirical Studies in an International Context

The present dissertation analyzed the construct of attachment at different time points, specifically focusing on two phases of adoptive family life that have so far received little attention from investigators. Study 1 focused on the first months of adoption, and analyzed the development of the attachment relationship to new caregivers. The sample was composed of a small but homogeneous group (n=6) of Korean-born children, adopted by Italian parents. The Parent Attachment Diary (Dozier & Stovall, 1997) was utilized to assess the child's attachment behavior. We assessed these behavior for the first 3 months after placement into adoption. Results showed a double variability of attachment behavior: within subjects during the 3-months, and between subjects, with just half of the children developing a stable pattern of attachment. In order to test the growth trajectories of attachment behavior, Hierarchical Linear Models (Bryk & Raudenbush, 1992) were also applied, but no significant population trend was identified. Study 2 analyzed attachment among adoptees during the sensitive period of adolescence. Data was derived from an international collection (n= 104, from Belgium Italy, and Romania) of semi-structured clinical interviews (with adolescents and with their adoptive parents), as well as from questionnaires. The purpose of this study was to detect the role played by risk and protective factors on the adoptee's behavioral and socio-emotional outcomes. In addition, we tested the possible interactions between the different attachment representations within the adoptive family. Results showed that pre-adoptive risk predicted the adolescent's adjustment; however, parental representations constituted an important moderator of this relationship. Moreover, the adolescent's security of attachment partially mediated the relationship between age at placement and later behavioral problems. In conclusion, the two present attachment studies highlighted the notable rate of change of attachment behavior over time, which showed its underlying plasticity, and thus the possible reparatory value of the adoption practice. Since parents have been proven to play an important role, especially in adolescence, the post-adoption support acquires even more importance in order to help parents promoting a positive and stable relational environment over time. - L'objectif de cette thèse est de décrire la formation des relations d'attachement chez les enfants et les adolescents adoptés, lors de deux phases particulières de la vie de la famille adoptive, qui ont été relativement peu étudiées. L'Étude 1 analyse les premiers mois après l'adoption, avec le but de comprendre si, et comment, une relation d'attachement aux nouveaux parents se développe. L'échantillon est composé d'un petit groupe (n = 6) d'enfants provenant de Corée du Sud, adoptés par des parents Italiens. A l'aide du Parent Attachment Diary (Dozier & Stovall, 1997), des observations des comportements d'attachement de l'enfant ont été recueillies chaque jour au cours des 3 premiers mois après l'arrivée. Les résultats montrent une double variabilité des comportements d'attachement: au niveau inter- et intra-individuel ; au premier de ces niveaux, seuleme la moitié des enfants parvient à développer un pattern stable d'attachement ; au niveau intra-individuel, les trajectoires de développement des comportements d'attachement ont été testées à l'aide de Modèles Linéaires Hiérarchiques (Bryk et Raudenbush, 1992), mais aucune tendance significative n'a pu être révélée. L'Étude 2 vise à analyser l'attachement chez des enfants adoptés dans l'enfance, lors de la période particulièrement sensible de l'adolescence. Les données sont issues d'un base de données internationale (n = 104, Belgique, Italie et Roumanie), composée d' entretiens cliniques semi-structurées (auprès de l'adolescents et des ses parents adoptifs), ainsi que de questionnaires. Les analyses statistiques visent à détecter la présence de facteurs de risque et de protection relativement à l'attachement et aux problèmes de comportement de l'enfant adopté. En outre, la présence d'interactions entre les représentations d'attachement des membres de la famille adoptive est évaluée. Les résultats montrent que les risques associés à la période pré-adoptive prédisent la qualité du bien-être de l'adolescent, mais les représentations parentales constituent un modérateur important de cette relation. En outre, la sécurité de l'attachement du jeune adopté médiatise partiellement la relation entre l'âge au moment du placement et les problèmes de comportement lors de l'adolescence. En conclusion, à l'aide de multiples données relatives à l'attachement, ces deux études soulignent son évolution notable au fil du temps, ce qui sous-tend la présence d'une certaine plasticité, et donc la possible valeur réparatrice de la pratique de l'adoption. Comme les parents semblent jouer un rôle important de ce point de vue, surtout à l'adolescence, cela renforce la notion d'un soutien post-adoption, en vue d'aider les parents à la promotion d'un environnement relationnel favorable et stable. - Il presente lavoro è volto ad analizzare l'attaccamento durante le due fasi della vita della famiglia adottiva che meno sono state indagate dalla letteratura. Lo Studio 1 aveva l'obiettivo di analizzare i primi mesi che seguono il collocamento del bambino, al fine di capire se e come una relazione di attaccamento verso i nuovi genitori si sviluppa. Il campione è composto da un piccolo gruppo (n = 6) di bambini provenienti dalla Corea del Sud e adottati da genitori italiani. Attraverso il Parent Attachment Diary (Stovall e Dozier, 1997) sono stati osservati quotidianamente, e per i primi tre mesi, i comportamenti di attaccamento del bambino. I risultati hanno mostrato una duplice variabilità: a livello intraindividuale (nell'arco dei 3 mesi), ed interindividuale, poiché solo la metà dei bambini ha sviluppato un pattern stabile di attaccamento. Per verificare le traiettorie di sviluppo di tali comportamenti, sono stati applicati i Modelli Lineari Gerarchici (Bryk & Raudenbush, 1992), che però non hanno stimato una tendenza significativa all'interno della popolazione. Obiettivo dello Studio 2 è stato quello di esaminare l'attaccamento nelle famiglie i cui figli adottivi si trovavano nella delicata fase adolescenziale. I dati, provenienti da una raccolta internazionale (n = 104, Belgio, Italia e Romania), erano costituiti da interviste cliniche semi-strutturate (con gli adolescenti e i propri genitori adottivi) e da questionari. Le analisi hanno indagato il ruolo dei fattori di rischio sullo sviluppo socio-emotivo e sugli eventuali problemi comportamentali dei ragazzi. Inoltre, sono state esaminate le possibili interazioni tra le diverse rappresentazioni di attaccamento dei membri della famiglia adottiva. I risultati hanno mostrato che il rischio pre-adottivo predice l'adattamento dell'adolescente, sebbene le rappresentazioni genitoriali costituiscano un importante moderatore di questa relazione. Inoltre, la sicurezza dell'attaccamento dell'adolescente media parzialmente la relazione tra età al momento dell'adozione e problemi comportamentali in adolescenza. In conclusione, attraverso i molteplici dati relativi all'attaccamento, i due studi ne hanno evidenziato il cambiamento nel tempo, a riprova della sua plasticità, e pertanto sottolineano il possibile valore riparativo dell'adozione. Dal momento che i genitori svolgono un ruolo importante, soprattutto in adolescenza, il supporto nel post- adozione diventa centrale per aiutarli a promuovere un ambiente relazionale favorevole e stabile nel tempo.

Missing value imputation in longitudinal measures of alcohol consumption

Attrition in longitudinal studies can lead to biased results. The study is motivated by the unexpected observation that alcohol consumption decreased despite increased availability, which may be due to sample attrition of heavy drinkers. Several imputation methods have been proposed, but rarely compared in longitudinal studies of alcohol consumption. The imputation of consumption level measurements is computationally particularly challenging due to alcohol consumption being a semi-continuous variable (dichotomous drinking status and continuous volume among drinkers), and the non-normality of data in the continuous part. Data come from a longitudinal study in Denmark with four waves (2003-2006) and 1771 individuals at baseline. Five techniques for missing data are compared: Last value carried forward (LVCF) was used as a single, and Hotdeck, Heckman modelling, multivariate imputation by chained equations (MICE), and a Bayesian approach as multiple imputation methods. Predictive mean matching was used to account for non-normality, where instead of imputing regression estimates, "real" observed values from similar cases are imputed. Methods were also compared by means of a simulated dataset. The simulation showed that the Bayesian approach yielded the most unbiased estimates for imputation. The finding of no increase in consumption levels despite a higher availability remained unaltered. Copyright (C) 2011 John Wiley & Sons, Ltd.

Demographic and geographic vascular risk factor differences in European young adults with ischemic stroke: the 15 cities young stroke study.

BACKGROUND AND PURPOSE: We compared among young patients with ischemic stroke the distribution of vascular risk factors among sex, age groups, and 3 distinct geographic regions in Europe. METHODS: We included patients with first-ever ischemic stroke aged 15 to 49 years from existing hospital- or population-based prospective or consecutive young stroke registries involving 15 cities in 12 countries. Geographic regions were defined as northern (Finland, Norway), central (Austria, Belgium, France, Germany, Hungary, The Netherlands, Switzerland), and southern (Greece, Italy, Turkey) Europe. Hierarchical regression models were used for comparisons. RESULTS: In the study cohort (n=3944), the 3 most frequent risk factors were current smoking (48.7%), dyslipidemia (45.8%), and hypertension (35.9%). Compared with central (n=1868; median age, 43 years) and northern (n=1330; median age, 44 years) European patients, southern Europeans (n=746; median age, 41 years) were younger. No sex difference emerged between the regions, male:female ratio being 0.7 in those aged <34 years and reaching 1.7 in those aged 45 to 49 years. After accounting for confounders, no risk-factor differences emerged at the region level. Compared with females, males were older and they more frequently had dyslipidemia or coronary heart disease, or were smokers, irrespective of region. In both sexes, prevalence of family history of stroke, dyslipidemia, smoking, hypertension, diabetes mellitus, coronary heart disease, peripheral arterial disease, and atrial fibrillation positively correlated with age across all regions. CONCLUSIONS: Primary preventive strategies for ischemic stroke in young adults-having high rate of modifiable risk factors-should be targeted according to sex and age at continental level.

The influence of drinking pattern, at individual and aggregate levels, on alcohol-related negative consequences.

AIM: To determine the extent drinking patterns (at the individual and country level) are associated with alcohol-related consequences over and above the total alcohol the person consumes. METHODS: Hierarchical linear models were estimated based on general population surveys conducted in 18 countries participating in the GENACIS project. RESULTS: In general, the positive association between drinking pattern scores and alcohol-related consequences was found at both the individual and country levels, independent of volume of drinking. In addition, a significant interaction effect indicated that the more detrimental the country's drinking pattern, the less steep the association between the volume of drinking and its consequences. CONCLUSION: Drinking patterns have an independent impact on consequences over and above the relationship between volume and consequences.

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Bayesian classification criterion for forensic multivariate data

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.

Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.

Object-oriented Bayesian networks for evaluating DIP-STR profiling results from unbalanced DNA mixtures.

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, with current methods for DNA analysis (Polymerase Chain Reaction with the SGM Plus™ multiplex kit), it is generally not possible to obtain a conventional autosomal DNA profile of the minor contributor if the ratio between the two contributors in a mixture is smaller than 1:10. This is a consequence of the fact that the major contributor's profile 'masks' that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP), linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed elsewhere in literature [1]. The present paper reports on the derivation of an approach for the probabilistic evaluation of DIP-STR profiling results obtained from unbalanced DNA mixtures. The procedure is based on object-oriented Bayesian networks (OOBNs) and uses the likelihood ratio as an expression of the probative value. OOBNs are retained in this paper because they allow one to provide a clear description of the genotypic configuration observed for the mixed stain as well as for the various potential contributors (e.g., victim and suspect). These models also allow one to depict the assumed relevance relationships and perform the necessary probabilistic computations.

Overcoming the rare species modelling paradox: test of a novel hierarchical framework with an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.