Model to evaluate the cost-effectiveness of different antibiotics in the management of acute bacterial exacerbations of chronic bronchitis in Germany

Summary

This study investigated the primary cost-drivers and determinants of the cost-effectiveness of antibacterial treatment of acute bacterial exacerbations of chronic bronchitis (ABECB) in Germany. It assessed the health care costs and consequences related to treatment initiated in the community using macrolides, fluoroquinolones, penicillins, and cephalosporins. Patients were categorised according to disease severity. Decision analysis was used to consider the clinical and economic consequences of various treatment options from first-line treatment initiated by a primary care physician in the community until success or failure after third-line treatment in hospital.

The key cost drivers were the clinical success/failure rates of first-line treatment and the cost per day of hospitalisation. Antibiotics with the cheapest purchase price are not necessarily the most cost-effective first-line treatment. In more severe ABECB, drug acquisition costs are only a small proportion of the total healthcare costs because the extra costs associated with treatment failure are much greater than the acquisition costs of the first-line antibiotics. Thus the most cost-effective first-line treatment is one which results in consistently high clinical effectiveness due to its broad spectrum of activity, low rate of bacterial resistance, and high patient compliance. Of the antibiotics considered, none was consistently found to be the most cost-effective treatment across the full range of scenarios investigated. However, the fluoroquinolones and cephalosporins were generally more cost-effective than the macrolides and penicillins.     

An improved spanning tree approach for the reliability analysis of supply chain collaborative network

A higher degree of reliability in the collaborative network can increase the competitiveness and performance of an entire supply chain. As supply chain networks grow more complex, the consequences of unreliable behaviour become increasingly severe in terms of cost, effort and time. Moreover, it is computationally difficult to calculate the network reliability of a Non-deterministic Polynomial-time hard (NP-hard) all-terminal network using state enumeration, as this may require a huge number of iterations for topology optimisation. Therefore, this paper proposes an alternative approach of an improved spanning tree for reliability analysis to help effectively evaluate and analyse the reliability of collaborative networks in supply chains and reduce the comparative computational complexity of algorithms. Set theory is employed to evaluate and model the all-terminal reliability of the improved spanning tree algorithm and present a case study of a supply chain used in lamp production to illustrate the application of the proposed approach.     

Calibration to American options: numerical investigation of the de-Americanization method

American options are the reference instruments for the model calibration of a large and important class of single stocks. For this task, a fast and accurate pricing algorithm is indispensable. The literature mainly discusses pricing methods for American options that are based on Monte Carlo, tree and partial differential equation methods. We present an alternative approach that has become popular under the name de-Americanization in the financial industry. The method is easy to implement and enjoys fast run-times (compared to a direct calibration to American options). Since it is based on ad hoc simplifications, however, theoretical results guaranteeing reliability are not available. To quantify the resulting methodological risk, we empirically test the performance of the de-Americanization method for calibration. We classify the scenarios in which de-Americanization performs very well. However, we also identify the cases where de-Americanization oversimplifies and can result in large errors.     

The Latino Assumption: A Research Note

The Latino assumption refers to the presumption that individuals residing in the United States and its commonwealths who have descended from Latin American countries and Spain constitute a homogeneous panethnic social identity. Inappropriately studying individuals of Latin American and Spanish descent as a homogeneous group in leisure research may result in threats to internal validity, statistical conclusion, and external validity. This article examines these issues and the historical context of the Latino assumption.     

树型网络中IPTV系统的流量均衡策略

提出一种在树型网络中的网络电视(IPTV)系统负载均衡算法,该算法通过服务器放置点的选择和服务器内容分配实现负载均衡.首先把大的树型网络分解成多个小的树型网络,对每个小的树型网络应用一种改进的搜索算法,最后把在多个子树的解组合成原问题的解,较好地将流量均匀分配到整个网络中.     

Is the ex‐ante equity risk premium always positive? Evidence from a new conditional expectations model

We model the conditional risk premium by combining principal component analysis and a statistical learning technique, known as boosted regression trees. The method is validated through various out‐of‐sample tests. We apply the estimates to test the positivity restriction on the risk premium and find evidence that the risk premium is negative in periods of low corporate and government bond returns, high inflation and downward‐sloping term structure. These periods are linked with changes in business cycles; the states when theories predict the existence of negative risk premium. Based on the evidence, we reject the conditional capital asset pricing model and raise a question over the practice of imposing the positive risk premium constraint in predictive models.     

Mapping technological development using patent citation trees: an analysis of bogie technology

We create a generational patent citation tree to reveal the development of bogie technology in the high-speed rail industry. We cull the information in patent citations to reveal the generational development of bogie technology by analysing patent titles, patent publication dates and patent-granting regions. We then measure the development of bogie technology by calculating its technological development on-orbit rate and by mapping the stages of technological development and its regional diffusion. The results show that bogie technologies are in a period of sustained growth and this period of sustained growth will remain. Technological development is accelerating and this acceleration will persist, although the global dispersion of technology lags behind in scope and speed.     

Environmental services auctions under regulatory threat

This paper examines how strategic responses of bidders and efficiency properties are impacted in auctions for the procurement of environmental services when a threat of regulation is levied. Laboratory experiments reveal characteristics of bidder behavior in different regulatory environments. Experimental results provide insight into efficiency and equity tradeoffs inherent in regulatory policy applications with respect to environmental services auctions. While it is possible to reduce the amount of public funds necessary to purchase a given level of environmental services, adverse selection costs and equity considerations may outstrip the benefits gained from threat implementation.     

Adversarial and Amiable Inference in Medical Diagnosis,Reliability and Survival Analysis

In this paper, we develop a family of bivariate beta distributions that encapsulate both positive and negative correlations, and which can be of general interest for Bayesian inference. We then invoke a use of these bivariate distributions in two contexts. The first is diagnostic testing in medicine, threat detection and signal processing. The second is system survivability assessment, relevant to engineering reliability and to survival analysis in biomedicine. In diagnostic testing, one encounters two parameters that characterize the efficacy of the testing mechanism: test sensitivity and test specificity. These tend to be adversarial when their values are interpreted as utilities. In system survivability, the parameters of interest are the component reliabilities, whose values when interpreted as utilities tend to exhibit co‐operative (amiable) behavior. Besides probability modeling and Bayesian inference, this paper has a foundational import. Specifically, it advocates a conceptual change in how one may think about reliability and survival analysis. The philosophical writings of de Finetti, Kolmogorov, Popper and Savage, when brought to bear on these topics constitute the essence of this change. Its consequence is that we have at hand a defensible framework for invoking Bayesian inferential methods in diagnostics, reliability and survival analysis. Another consequence is a deeper appreciation of the judgment of independent lifetimes. Specifically, we make the important point that independent lifetimes entail at a minimum, a two‐stage hierarchical construction.     

NONPARAMETRIC MODELS OF FINANCIAL LEVERAGE DECISIONS

This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two‐part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small.