Technologies de prescription informatisée et transformation du rôle des pharmaciens communautaires

L’amélioration de la qualité de l’utilisation des médicaments dans les soins primaires est devenue un enjeu crucial. Les pharmaciens communautaires se présentent comme des acteurs centraux dans l’atteinte de cet objectif, en réclamant une extension de leur rôle. L’objectif principal de cette thèse est de mieux comprendre comment les technologies de prescription informatisée (eRx) influencent la transformation du rôle des pharmaciens communautaires. Le premier article présente les résultats d’une étude de cas qui aborde la transformation du rôle des pharmaciens communautaires à partir du concept de professionnalisation. Elle propose un modèle logique des influences d’une technologie de eRx sur cette professionnalisation, élaboré à partir de la typologie de Davenport. Ce modèle logique a été validé en interviewant douze pharmaciens communautaires participant à un projet pilote typique de technologie de eRx. A partir des perceptions des pharmaciens communautaires, nous avons établi que la technologie était susceptible de soutenir la professionnalisation des pharmaciens en passant par cinq mécanismes : la capacité analytique, l’élimination des intermédiaires, l’intégration, l’automatisation et la diffusion des connaissances. Le deuxième article analyse les perturbations induites par les différentes fonctions des technologies de eRx sur la stabilité de la juridiction des pharmaciens communautaires, en se basant sur un cadre de référence adapté d’Abbott. À partir de trente-trois entrevues, avec des praticiens (médecins et pharmaciens) et des élites, cette étude de cas a permis de décrire en détail les influences des différentes fonctions sur les modalités d’action des professionnels, ainsi que les enjeux soulevés par ces possibilités. La perturbation principale est liée aux changements dans la distribution des informations, ce qui influence les activités de diagnostic et d’inférence des professionnels. La technologie peut redistribuer les informations relatives à la gestion des médicaments autant au bénéfice des médecins qu’au bénéfice des pharmaciens, ce qui suscite des tensions entre les médecins et les pharmaciens, mais aussi parmi les pharmaciens. Le troisième article présente une revue systématique visant à faire une synthèse des études ayant évalué les effets des technologies de eRx de deuxième génération sur la gestion des médicaments dans les soins primaires. Cette revue regroupe dix-neuf études menées avec des méthodes observationnelles. Les résultats rapportés révèlent que les technologies sont très hétérogènes, le plus souvent immatures, et que les effets ont été peu étudiés au-delà des perceptions des utilisateurs, qui sont mitigées. Le seul effet positif démontré est une amélioration de la qualité du profil pharmacologique accessible aux professionnels, alors que des effets négatifs ont été démontrés au niveau de l’exécution des prescriptions, tels que l’augmentation du nombre d’appels de clarification du pharmacien au prescripteur. Il semble donc que l’on en connaisse peu sur les effets des technologies de eRx de deuxième génération. Ces trois études permettent de constater que les nouvelles technologies de eRx peuvent effectivement influencer la transformation du rôle du pharmacien communautaire en perturbant les caractéristiques des prescriptions, et surtout, l’information et sa distribution. Ces perturbations génèrent des possibilités pour une extension du rôle des pharmaciens communautaires, tout en soulignant les défis intra et interprofessionnels associés à l’actualisation de ces possibilités. Dans l’ensemble, nos résultats soulignent que les perturbations associées aux technologies de eRx dépassent les éléments techniques du travail des utilisateurs, pour englober de multiples perturbations quant à la nature même du travail et du rôle des professionnels. Les décideurs et acteurs impliqués dans le déploiement des technologies de eRx auraient avantage à prendre en compte l’ensemble de ces considérations pour rapprocher les effets observés des bénéfices promis de ces technologies.

L’analogie de l’hérédité culturelle : fondements conceptuels de la théorie de la double hérédité

Selon la théorie de la double hérédité, les processus de transmission sociale des connaissances permettraient aux cultures humaines d'évoluer de manière darwinienne. On parvient à cette conclusion en inférant que, étant donné qu'une analogie profonde peut être établie entre les mécanismes de transmission génétique et ceux de transmission sociale, on devrait non seulement concevoir que les processus cognitifs d'apprentissage social constituent bel et bien un système d'hérédité distinct du système d'hérédité génétique, mais qu’il est aussi légitime, sur la base de cette même analogie, de transférer les concepts explicatifs et outils formels issus de la biologie évolutionnaire et de les adapter à l'étude des cultures humaines en vue de constituer une théorie darwinienne de l'évolution culturelle. Cette analogie de l’hérédité culturelle fait depuis longtemps l'objet de controverses tant au sein de la littérature scientifique que dans les discussions philosophiques. On ne semble pas s'entendre sur la nature même de cette analogie ni non plus sur la force de justification épistémique qu'une telle analogie donnerait à la mise en place d'une théorie darwinienne de l'évolution culturelle. Néanmoins, à travers plus de quarante années de débats, la structure de cette analogie n'a jamais été examinée en détail et on a rarement examiné l'épistémologie des inférences par analogie dans un tel contexte. L'objectif principal de la présente thèse consistera à offrir une première analyse systématique de la nature, de la structure, de la fonction et de la justification épistémique de l'analogie de l'hérédité culturelle, fondement conceptuel de la théorie de la double hérédité. En portant ici une attention particulière à la structure logique de cette analogie, on pourra constater l'ampleur de sa complexité, complexité passant souvent inaperçue dans les critiques de la théorie de la double hérédité. On défendra ici la thèse selon laquelle l'analogie de l'hérédité culturelle est en fait composée de deux analogies constitutives qui, conjointement, ouvrent la voie à la mise en place et à l’organisation d’un programme de recherche visant à mettre au point une théorie darwinienne de l’évolution culturelle.

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust, and thus to farmers trust toward cooperatives. We propose a model that sustain these empirical facts. A model where the cooperative reputation is a leading factor determining the market equilibrium of a price competition between a cooperative and a private firm

Bayesian inference and model comparison for random choice structures

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions are joint distributions over various choice distributions over choice sets of di fferent sizes. Since choice distributions over di fferent choice sets can be mutually dependent, previous methods relying on conjugate prior distributions do not apply. We demonstrate by analyzing data from a previously reported experiment and report evidence for and against various axioms.

Inference for nonparametric high-frequency estimators with an application to time variation in betas

We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. We suggest a procedure for a data-driven choice of the bandwidth parameters. Our simulation study indicates that the subsampling method is much more robust than the plug-in method based on the asymptotic expression for the variance. Importantly, the subsampling method reliably estimates the variability of the Two Scale estimator even when its parameters are chosen to minimize the finite sample Mean Squared Error; in contrast, the plugin estimator substantially underestimates the sampling uncertainty. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semi-definite. We use the subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas within year 2006, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every five or twenty minutes. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe.

Statistical Inference for Some Measures of System Availability

Department of Statistics, Cochin University of Science and Technology

Bayesian Inference in Exponential and Pareto populations in the presence of Outliers

This thesis Entitled Bayesian inference in Exponential and pareto populations in the presence of outliers. The main theme of the present thesis is focussed on various estimation problems using the Bayesian appraoch, falling under the general category of accommodation procedures for analysing Pareto data containing outlier. In Chapter II. the problem of estimation of parameters in the classical Pareto distribution specified by the density function. In Chapter IV. we discuss the estimation of (1.19) when the sample contain a known number of outliers under three different data generating mechanisms, viz. the exchangeable model. Chapter V the prediction of a future observation based on a random sample that contains one contaminant. Chapter VI is devoted to the study of estimation problems concerning the exponential parameters under a k-outlier model.

A Theory of Quantitative Inference Applied to a Mechanical Design Compiler

This thesis presents the ideas underlying a computer program that takes as input a schematic of a mechanical or hydraulic power transmission system, plus specifications and a utility function, and returns catalog numbers from predefined catalogs for the optimal selection of components implementing the design. Unlike programs for designing single components or systems, the program provides the designer with a high level "language" in which to compose new designs. It then performs some of the detailed design process. The process of "compilation" is based on a formalization of quantitative inferences about hierarchically organized sets of artifacts and operating conditions. This allows the design compilation without the exhaustive enumeration of alternatives.

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

The Logical Problem of Language Change

This paper considers the problem of language change. Linguists must explain not only how languages are learned but also how and why they have evolved along certain trajectories and not others. While the language learning problem has focused on the behavior of individuals and how they acquire a particular grammar from a class of grammars ${cal G}$, here we consider a population of such learners and investigate the emergent, global population characteristics of linguistic communities over several generations. We argue that language change follows logically from specific assumptions about grammatical theories and learning paradigms. In particular, we are able to transform parameterized theories and memoryless acquisition algorithms into grammatical dynamical systems, whose evolution depicts a population's evolving linguistic composition. We investigate the linguistic and computational consequences of this model, showing that the formalization allows one to ask questions about diachronic that one otherwise could not ask, such as the effect of varying initial conditions on the resulting diachronic trajectories. From a more programmatic perspective, we give an example of how the dynamical system model for language change can serve as a way to distinguish among alternative grammatical theories, introducing a formal diachronic adequacy criterion for linguistic theories.

Memory Usage Inference for Object-Oriented Programs

We present a type-based approach to statically derive symbolic closed-form formulae that characterize the bounds of heap memory usages of programs written in object-oriented languages. Given a program with size and alias annotations, our inference system will compute the amount of memory required by the methods to execute successfully as well as the amount of memory released when methods return. The obtained analysis results are useful for networked devices with limited computational resources as well as embedded software.

Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detection limit, which is the minimum concentration that conclusively differentiates between presence and absence of the element. A spatially distributed exhaustive sample is employed in this study to generate unbiased sub-samples, which are further censored to observe the effect that different detection limits and sample sizes have on the inference of population distributions starting from geochemical analyses having specimens below detection limit (nondetects). The isometric logratio transformation is used to convert the compositional data in the simplex to samples in real space, thus allowing the practitioner to properly borrow from the large source of statistical techniques valid only in real space. The bootstrap method is used to numerically investigate the reliability of inferring several distributional parameters employing different forms of imputation for the censored data. The case study illustrates that, in general, best results are obtained when imputations are made using the distribution best fitting the readings above detection limit and exposes the problems of other more widely used practices. When the sample is spatially correlated, it is necessary to combine the bootstrap with stochastic simulation

Automatic self-configuration of the logical network using distributed software agents

We present a system for dynamic network resource configuration in environments with bandwidth reservation. The proposed system is completely distributed and automates the mechanisms for adapting the logical network to the offered load. The system is able to manage dynamically a logical network such as a virtual path network in ATM or a label switched path network in MPLS or GMPLS. The system design and implementation is based on a multi-agent system (MAS) which make the decisions of when and how to change a logical path. Despite the lack of a centralised global network view, results show that MAS manages the network resources effectively, reducing the connection blocking probability and, therefore, achieving better utilisation of network resources. We also include details of its architecture and implementation