Bücher zum Thema „OMOP common data model“

In September 2007 the national team members of the International Inforum (Interindustry Forecasting Project at the University of Maryland) group held the XV annual World Conference in Truijllo, Spain. Such Conferences offer the participants to report their achievements in the different fields concerning the macroeconomic multisectoral modeling approach and data development. The national partners build their country model based on a common input-output accounting structure and a similar econometric modeling approach for sectoral and macroeconomic variables. In each Conference, the contributions refer to the wide spectrum of research activities carried on within the Inforum system of models.

The book presents developmental outcomes from an EU Erasmus+ project involving eight partner universities in seven countries in Europe. Its focus is the development of mathematics teaching and learning at university level to enhance the learning of mathematics by university students. Its theoretical focus is inquiry-based teaching and learning. It bases all activity on a three-layer model of inquiry: (1) Inquiry in mathematics and in the learning of mathematics in lecture, tutorial, seminar or workshop, involving students and teachers; (2) Inquiry in mathematics teaching involving teachers exploring and developing their own practices in teaching mathematics; (3) Inquiry as a research process, analysing data from layers (1) and (2) to advance knowledge inthe field. As required by the Erasmus+ programme, it defines Intellectual Outputs (IOs) that will develop in the project. PLATINUM has six IOs: The Inquiry-based developmental model; Inquiry communities in mathematics learning and teaching; Design of mathematics tasks and teaching units; Inquiry-based professional development activity; Modelling as an inquiry process; Evalutation of inquiry activity with students. The project has developed Inquiry Communities, in each of the partner groups, in which mathematicians and educators work together in supportive collegial ways to promote inquiry processes in mathematics learning and teaching. Through involving students in inquiry activities, PLATINUM aims to encourage students` own in-depth engagement with mathematics, so that they develop conceptual understandings which go beyond memorisation and the use of procedures. Indeed the eight partners together have formed an inquiry community, working together to achieve PLATINUM goals within the specific environments of their own institutions and cultures. Together we learn from what we are able to achieve with respect to both common goals and diverse environments, bringing a richness of experience and learning to this important area of education. Inquiry communities enable participants to address the tensions and issues that emerge in developmental processes and to recognise the critical nature of the developmental process. Through engaging in inquiry-based development, partners are enabled and motivated to design activities for their peers, and for newcomers to university teaching of mathematics, to encourage their participation in new forms of teaching, design of teaching, and activities for students. Such professional development design is an important outcome of PLATINUM. One important area of inquiry-based activity is that of “modelling” in mathematics. Partners have worked together across the project to investigate the nature of modelling activities and their use with students. Overall, the project evaluates its activity in these various parts to gain insights to the sucess of inquiry based teaching, learning and development as well as the issues and tensions that are faced in putting into practice its aims and goals.

This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, the standard Levenberg-Marquardt method is described, followed by the use of optimal estimation, one of several retrieval methods that make use of a priori information to providing regularization for the solution. In the context of optimal estimation, weighting functions, contribution functions, and averaging kernels are described. The Twomey-Tikhonov regularization procedure is presented. Correlated parameters, with the important example of Earth’s atmospheric ozone, are discussed.

This chapter introduces embedded models. This is a special case of a parametric model which cannot be obtained simply by setting the parameters to particular values in a simple way. An example is the regression function y = b[1−exp(−ax)], which is always curved when a and b have fixed values. But letting a tend to zero and b tend to infinity simultaneously, whilst keeping ab = c fixed, yields y = cx, a straight-line special case. When this is the true model, fitting the original two-parameter model leads to very unstable and individually meaningless estimates of a and b. Such embedded models are actually very common in the literature, leading to confusion in interpretation of results when undetected. In this chapter, embeddedness is defined and a large number of regression embedded model examples given. Detection and removal of embeddedness by reparametrization is discussed. Two real data numerical examples are given.

This chapter focuses upon the needs of two approaches, economic evaluations based on decision analytic models, and those alongside clinical trials in terms of the collection and analysis of health state values. The first section of the chapter presents requirements that are likely to be common to any study in which health state values are collected from patients and/or members of the general population, including: who to ask, mode of administration, timing of assessments, sample size, and handling uncertainty. The second section of the chapter considers issues specific to trial-based economic evaluations, and the final section considers issues specific to the design and analysis of health state valuation data for economic models.

This chapter discusses recovering shape or structural information from SAXS data. Key to any such process is the ability to generate a calculated intensity from a model, and to compare this curve with the experimental one. Models for the particle scattering density can be approximated as pure homogenenous geometric shapes. More complex particle surfaces can be represented by spherical harmonics or by a set of close-packed beads. Sometimes structural information is known for components of a particle. Rigid body modeling attempts to rotate and translate structures relative to one another, such that the resulting scattering profile calculated from the model agrees with the experimental SAXS data. More advanced hybrid modelling procedures aim to incorporate as much structural information as is available, including modelling protein dynamics. Solutions may not always contain a homogeneous set of particles. A common case is the presence of two or more conformations of a single particle or a mixture of oligomeric species. The method of singular value decomposition can extract scattering for conformationally distinct species.

This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.

This chapter explores statistical and graphical strategies for exploring the effect of interventions in health research and presents a brief overview of some common and some not so common methods for assessing data from interventions and have pointed to some associated advantages and disadvantages. It discusses gain scores, analysis of variance models, structural equation modelling, and the latent growth model.

This chapter examines patterns of comorbidity between eating disorders and mood, anxiety, and substance use disorders along with evidence regarding support for different theoretical models that may account for these patterns. Although comorbidity estimates may be inflated by reliance on treatment-seeking samples and double counting of symptoms that overlap between syndromes, evidence supports elevated risk of mood, anxiety, and substance use disorders in anorexia nervosa, bulimia nervosa, and binge eating disorder. Data from family and twin studies support that eating and anxiety disorders may have a shared diathesis, consistent with the common cause model. Data from longitudinal studies suggest that eating disorders may increase vulnerability for developing a substance use disorder, consistent with the predisposition model. In contrast, comorbidity between eating and mood disorders, such as depression, remains poorly understood. Clinical issues regarding comorbidity of depression and eating disorders along with guidelines for clinicians treating patients with comorbid depression and eating disorders are discussed.

Modern practice of ecology, conservation, and resource management demands unprecedented levels of quantitative proficiency in mathematical modeling and statistics. This text provides foundational training in the concepts and methods of mathematical and statistical modeling used in ecology, for readers with all levels of quantitative proficiency and confidence. The first chapter presents a generalized approach to develop ecological models and introduces the “describe, explain, and interpret” framework for linking the model world to the real world. Detailed treatment of population models illustrates the myriad ways in which one can develop a model, shows how modeling choices are informed by the ecological question at hand, and emphasizes the epistemology of quantitative techniques. The second part of the book illustrates how to estimate parameters of models from data, and how to use mathematical models combined with statistics to test hypotheses. The third part of the book is devoted to an in-depth development of technical skills to implement models in two common platforms: spreadsheets and the R programming language. The book concludes by demonstrating a quantitative approach to addressing a question that spans density-dependent versus density-independent population models, fitting models to data, evaluating the strength for density dependence using model selection, and evaluating the types of dynamic behaviors that the population might exhibit.

This chapter will critically examine today’s common ways to build equilibrium models. These specifically include Dynamic-Stochastic General Equilibrium models, game theoretical models and empirical GE models. Each of these types of equilibrium model try to address the issues of how a model’s decision makers get the information needed to guarantee the attainment of a state of equilibrium. The chapter addresses the alleged limits of general equilibrium models (particularly the issues of dynamics, time and expectations), the current attempts to overcome the limits of general equilibrium models, and three empirical alternatives to Walrasian general equilibrium models. These alternatives include the Computable General Equilibrium models and the Applied General Equilibrium models. The third model involves building econometric models only after evaluating the statistical properties of the data before using them in the model.

New Public Management (NPM) reforms have been around in many countries for over the past 30 years. NPM is an ambiguous, multifaceted, and expanded concept. There is not a single driving force behind it, but rather a mixture of structural and polity features, national historical-institutional contexts, external pressures, and deliberate choices from political and administrative executives. NPM is not the only show in town, and contextual features matter. There is no convergence toward one common NPM model, but significant variations exist between countries, government levels, policy areas, tasks, and over time. Its effects have been found to be ambiguous, inconclusive, and contested. Generally, there is a lack of reliable data on results and implications, and there is some way to go before one can claim evidence-based policymaking in this field. There is more knowledge regarding NPM’s effects on processes and activities than on outcome, and reliable comparative data on variations over time and across countries are missing. NPM has enhanced managerial accountability and accountability to users and customers, but has this success been at the expense of political accountability? New trends in reforms, such as whole-of-government, have been added to NPM, thereby making public administration more complex and hybrid.

This book presents a unified approach to evolutionary psychopathology, and advances an integrative framework for the analysis and classification of mental disorders based on the concepts of life history theory. The framework does not aim to replace existing evolutionary models of specific disorders—which are reviewed and critically discussed in the book—but to connect them in a broader perspective and explain the large-scale patterns of risk and comorbidity that characterize psychopathology. The life history framework permits a seamless integration of mental disorders with normative individual differences in personality and cognition, and offers new conceptual tools for the analysis of developmental, genetic, and neurobiological data. The concepts synthesized in the book are used to derive a new taxonomy of mental disorders, the fast-slow-defense (FSD) model. The FSD model is the first classification system explicitly based on evolutionary concepts, a biologically grounded alternative to transdiagnostic models based on empirical correlations between symptoms. The book reviews a wide range of common mental disorders, discusses their classification in the FSD model, and identifies functional subtypes within existing diagnostic categories.

Natural resources are often overexploited. Nevertheless, there are counterexamples of sustainably using common-pool resources. This book analyses the most important factors influencing the management of natural resources. Hence, the important question—What makes some systems successful?—is answered in this book. Based on three of the world’s largest data sets on fisheries, forest management, and irrigation systems, success factors are empirically examined. The book presents a synthesis of twenty-four success factors that explain ecological success, such as participation possibilities. The analysis in this book uses a range of statistical and machine learning methods to develop highly predictive, robust, and empirically sound models that shed new light on factors that have already been investigated. From this analysis the author develops a general model which can predict the success of in natural resource management very well, depending on the identified success factors.

Psychiatry, once proud of its biopsychosocial model, has now adopted a neuroscience-based approach that strongly favors psychopharmacological treatments and downplays the role of psychotherapies (or social interventions). This kind of practice can be sufficient for the psychoses, but it is neither evidence-based nor beneficial for patients with common mental disorders such as depression, anxiety, substance use, and personality disorders. Current practice derives from a theoretical model in which psychiatry is viewed primarily as an application of neuroscience, with little reference to the vast literature on psychology, social sciences, and psychotherapy. This book reviews research bearing on these issues, and it shows why existing data support a different set of conclusions from those held by many experts and most practitioners. Common mental disorders often require psychological interventions. This book reviews the evidence supporting the prescription of psychotherapy in these disorders. It shows how this option, particularly when time-limited, is both accessible and cost-effective. It discusses why psychiatrists are not offering psychological treatments, as well as the problem of access to these interventions. It also discusses why psychotherapy, like psychopharmacology, should not be considered a panacea for every kind of clinical problem but, rather, should be prescribed with care based on a large body of scientific evidence.

Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the unobserved causal parameter from observed data. Control time series are chosen so as to render plausible threats to internal validity implausible. An appropriate control time series may not exist, however, an ideal time series may be possible to construct. Synthetic control group models construct a control time series that optimally recreates the treated unit’s preintervention trend using a combination of untreated donor pool units.

This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an introduction to credit risk assessment and predictive modelling; 2) micro-histories of credit, credit intelligence, credit scoring, plus industrial revolutions, economic ups and downs, and both personal registration and identification; 4) mathematical and statistical tools used to develop and assess predictive models; 5) project management and data assembly; 6) data preparation from sampling to reject inference; 7) model training through to implementation; and 8) appendices, including an extensive glossary, bibliography, and index. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines as diverse as psychology, biology, engineering, and computer science, whether academic research or practical use. It also covers issues relating to the use of machine learning for credit risk assessment. Most of the focus is on traditional modelling techniques, but the increasing use of machine learning is recognised, as are its limitations. It is hoped that the contents will inform both camps.

The liver with its parenchymal and non-parenchymal cells plays a key role in the organism with manifold functions of metabolism, synthesis, detoxification, excretion, and host response. This requires a portfolio of different tests to obtain an overview of hepatic function. In the critically ill hepatic dysfunction is common and potentially leading to extrahepatic organ dysfunctions culminating in multi-organ failure. Conventional laboratory measures are used to evaluate hepatocellular damage, cholestasis, or synthesis. They provide valuable (differential) diagnostic data and can yield prognostic information in chronic liver diseases, especially when used in scoring systems such as the ‘model for end-stage liver disease’. However, they have short-comings in the critically ill in assessing rapid changes in hepatic function and liver blood flow. In contrast, dynamic quantitative liver function tests measure current liver function with respect to the ability to eliminate and/or metabolize a specific substance. In addition, they are dependent on sinusoidal blood flow. Liver function tests have prognostic significance in the critically ill and may be used to guide therapy.

According to a widely shared notion, foreign affairs are exempted from democratic politics, i.e., party-political divisions are overcome—and should be overcome—for the sake of a common national interest. This book shows that this is not the case. Examining votes in the US Congress and several European parliaments, the book demonstrates that contestation over foreign affairs is barely different from contestation over domestic politics. Analyses of a new collection of deployment votes, of party manifestos, and of expert survey data show that political parties differ systematically over foreign policy and military interventions in particular. The left/right divide is the best guide to the pattern of party-political contestation: support is weakest at the far left of the spectrum and increases as one moves along the left/right axis to green, social democratic, liberal, and conservative parties; amongst parties of the far right, support is again weaker than amongst parties of the centre. An analysis of parliamentary debates in Canada, Germany, and the United Kingdom about the interventions in Afghanistan and against Daesh in Iraq and Syria shows that political parties also differ systematically in how they frame the use of force abroad. For example, parties on the right tend to frame their country’s participation in the US-led missions in terms of national security and national interests whereas parties on the left tend to engage in ‘spiral model thinking’, i.e., they critically reflect on the unintended consequences of the use of force in fuelling the conflicts with the Taliban and Daesh.

Abstract Co-operative businesses, which are democratically owned by their workers, customers, or suppliers, are rarer at scale in the United States than in most other rich democracies. Why? Despite the fact that co-operatives have long captured the interest of both American and international activists and social scientists alike, there has been scant effort to conduct systematic comparative analyses to answer this question. Offering the only rigorously designed comparative-historical analysis of how co-operative enterprise is institutionalized in different national contexts, this book draws on data from interviews and from more than two centuries of historical sources to compare the United States to three other rich democracies where co-operatives at scale are more common: Finland, France, and New Zealand. The book shows how the joint force of economic liberalism and racism acted historically to undermine the development of American co-operatives, leaving a legacy which can be traced forward to the present day. In the comparative success cases, co-operatives were better able to leverage resources and structural traits to animate the solidarity mechanism at the heart of the co-operative business model. In so doing, co-operatives in these countries were also better able to organize to achieve accommodative law and policy treatment over time. In addition, in examining the factors which variably undermined or advanced cooperation at scale in different contexts, the book tests two competing social science frameworks on institutions and organizations—field theory (strategic action fields) and historical institutionalism—and shows how the former enables a more comprehensive analysis and explanation.

The aim of the paper was to determine the influence of root systems of chosen tree species found in the Polish Flysch Carpathians on the increase of soil shear strength (root cohesion) in terms of slope stability. The paper's goal was achieved through comprehensive tests on root systems of eight relatively common in the Polish Flysch Carpathians tree species. The tests that were carried out included field work, laboratory work and analytical calculations. As part of the field work, the root area ratio (A IA) of the roots was determined using the method of profiling the walls of the trench at a distance of about 1.0 m from the tree trunk. The width of the. trenches was about 1.0 m, and their depth depended on the ground conditions and ranged from 0.6 to 1.0 m below the ground level. After preparing the walls of the trench, the profile was divided into vertical layers with a height of 0.1 m, within which root diameters were measured. Roots with diameters from 1 to 10 mm were taken into consideration in root area ratio calculations in accordance with the generally accepted methodology for this type of tests. These measurements were made in Biegnik (silver fir), Ropica Polska (silver birch, black locust) and Szymbark (silver birch, European beech, European hornbeam, silver fir, sycamore maple, Scots pine, European spruce) located near Gorlice (The Low Beskids) in areas with unplanned forest management. In case of each tested tree species the samples of roots were taken, transported to the laboratory and then saturated with water for at least one day. Before testing the samples were obtained from the water and stretched in a. tensile testing machine in order to determine their tensile strength and flexibility. In general, over 2200 root samples were tested. The results of tests on root area ratio of root systems and their tensile strength were used to determine the value of increase in shear strength of the soils, called root cohesion. To this purpose a classic Wu-Waldron calculation model was used as well as two types of bundle models, the so called static model (Fiber Bundle Model — FIRM, FBM2, FBM3) and the deformation model (Root Bundle Model— RBM1, RBM2, mRBM1) that differ in terms of the assumptions concerning the way the tensile force is distributed to the roots as well as the range of parameters taken into account during calculations. The stability analysis of 8 landslides in forest areas of Cicikowicleie and Wignickie Foothills was a form of verification of relevance of the obtained calculation results. The results of tests on root area ratio in the profile showed that, as expected, the number of roots in the soil profile and their ApIA values are very variable. It was shown that the values of the root area ratio of the tested tree species with a diameter 1-10 ram are a maximum of 0.8% close to the surface of the ground and they decrease along with the depth reaching the values at least one order of magnitude lower than close to the surface at the depth 0.5-1.0 m below the ground level. Average values of the root area ratio within the soil profile were from 0.05 to 0.13% adequately for Scots pine and European beech. The measured values of the root area ratio are relatively low in relation to the values of this parameter given in literature, which is probably connected with great cohesiveness of the soils and the fact that there were a lot of rock fragments in the soil, where the tests were carried out. Calculation results of the Gale-Grigal function indicate that a distribution of roots in the soil profile is similar for the tested species, apart from the silver fir from Bie§nik and European hornbeam. Considering the number of roots, their distribution in the soil profile and the root area ratio it appears that — considering slope stability — the root systems of European beech and black locust are the most optimal, which coincides with tests results given in literature. The results of tensile strength tests showed that the roots of the tested tree species have different tensile strength. The roots of European beech and European hornbeam had high tensile strength, whereas the roots of conifers and silver birch in deciduous trees — low. The analysis of test results also showed that the roots of the studied tree species are characterized by high variability of mechanical properties. The values Of shear strength increase are mainly related to the number and size (diameter) of the roots in the soil profile as well as their tensile strength and pullout resistance, although they can also result from the used calculation method (calculation model). The tests showed that the distribution of roots in the soil and their tensile strength are characterized by large variability, which allows the conclusion that using typical geotechnical calculations, which take into consideration the role of root systems is exposed to a high risk of overestimating their influence on the soil reinforcement. hence, while determining or assuming the increase in shear strength of soil reinforced with roots (root cohesion) for design calculations, a conservative (careful) approach that includes the most unfavourable values of this parameter should be used. Tests showed that the values of shear strength increase of the soil reinforced with roots calculated using Wu-Waldron model in extreme cases are three times higher than the values calculated using bundle models. In general, the most conservative calculation results of the shear strength increase were obtained using deformation bundle models: RBM2 (RBMw) or mRBM1. RBM2 model considers the variability of strength characteristics of soils described by Weibull survival function and in most cases gives the lowest values of the shear strength increase, which usually constitute 50% of the values of shear strength increase determined using classic Wu-Waldron model. Whereas the second model (mRBM1.) considers averaged values of roots strength parameters as well as the possibility that two main mechanism of destruction of a root bundle - rupture and pulling out - can occur at the same. time. The values of shear strength increase calculated using this model were the lowest in case of beech and hornbeam roots, which had high tensile strength. It indicates that in the surface part of the profile (down to 0.2 m below the ground level), primarily in case of deciduous trees, the main mechanism of failure of the root bundle will be pulling out. However, this model requires the knowledge of a much greater number of geometrical parameters of roots and geotechnical parameters of soil, and additionally it is very sensitive to input data. Therefore, it seems practical to use the RBM2 model to assess the influence of roots on the soil shear strength increase, and in order to obtain safe results of calculations in the surface part of the profile, the Weibull shape coefficient equal to 1.0 can be assumed. On the other hand, the Wu-Waldron model can be used for the initial assessment of the shear strength increase of soil reinforced with roots in the situation, where the deformation properties of the root system and its interaction with the soil are not considered, although the values of the shear strength increase calculated using this model should be corrected and reduced by half. Test results indicate that in terms of slope stability the root systems of beech and hornbeam have the most favourable properties - their maximum effect of soil reinforcement in the profile to the depth of 0.5 m does not usually exceed 30 kPa, and to the depth of 1 m - 20 kPa. The root systems of conifers have the least impact on the slope reinforcement, usually increasing the soil shear strength by less than 5 kPa. These values coincide to a large extent with the range of shear strength increase obtained from the direct shear test as well as results of stability analysis given in literature and carried out as part of this work. The analysis of the literature indicates that the methods of measuring tree's root systems as well as their interpretation are very different, which often limits the possibilities of comparing test results. This indicates the need to systematize this type of tests and for this purpose a root distribution model (RDM) can be used, which can be integrated with any deformation bundle model (RBM). A combination of these two calculation models allows the range of soil reinforcement around trees to be determined and this information might be used in practice, while planning bioengineering procedures in areas exposed to surface mass movements. The functionality of this solution can be increased by considering the dynamics of plant develop¬ment in the calculations. This, however, requires conducting this type of research in order to obtain more data.