Medicare private contracting: increasing patient choice and access?

Congress modified the Medicare program through the Balanced Budget Act of 1997 to expand patient choices for payment to physicians and certain other practitioners by allowing private contracting. This represents a shift in policy that has broad consequences for health care financing and program integrity. The effect of private contracting on quality and access to care remains unknown. Quality and access should be the most important measures of its success or failure. Out of pocket costs to seniors and vulnerable patients must also be watched closely.     

Principles for Conduct of Pest Risk Analyses: Report of an Expert Workshop

The North American Free Trade Agreement (NAFTA) and the General Agreement on Tariffs and Trade (GATT) have focused attention on risk assessment of potential insect, weed, and animal pests and diseases of livestock. These risks have traditionally been addressed through quarantine protocols ranging from limits on the geographical areas from which a product may originate, postharvest disinfestation procedures like fumigation, and inspections at points of export and import, to outright bans. To ensure that plant and animal protection measures are not used as nontariff trade barriers, GATT and NAFTA require pest risk analysis (PRA) to support quarantine decisions. The increased emphasis on PRA has spurred multiple efforts at the national and international level to design frameworks for the conduct of these analyses. As approaches to pest risk analysis proliferate, and the importance of the analyses grows, concerns have arisen about the scientific and technical conduct of pest risk analysis. In January of 1997, the Harvard Center for Risk Analysis (HCRA) held an invitation-only workshop in Washington, D.C. to bring experts in risk analysis and pest characterization together to develop general principles for pest risk analysis. Workshop participants examined current frameworks for PRA, discussed strengths and weaknesses of the approaches, and formulated principles, based on years of experience with risk analysis in other setting and knowledge of the issues specific to analysis of pests. The principles developed highlight the both the similarities of pest risk analysis to other forms of risk analysis, and its unique attributes.     

A queuing-theory-based approach to evaluate the efficiency of a network of automated stations and of a communication system

The Regional Meteorological Service for the Emilia-Romagna Region manages a network of automatic weather stations equipped with electronic sensors suitable for measuring meteorological parameters. The automatic stations consist of electronic instruments, which are subject to failures at more or less frequent intervals. A summary of their performance is necessary. In this paper, we compare the results of the summary, such as the contiguous absence or simultaneous inactivity of different stations, with theoretical simulations in order to evaluate the nature and recurrence of the failures. A single- and multi-server queue simulation model was also used to evaluate the performance of the data transmission system, so as to optimize the communications system.     

Multiple roots of estimating functions

Estimating functions can have multiple roots. In such cases, the statistician must choose among the roots to estimate the parameter. Standard asymptotic theory shows that in a wide variety of cases, there exists a unique consistent root, and that this root will lie asymptotically close to other consistent (possibly inefficient) estimators for the parameter. For this reason, attention has largely focused on the problem of selecting this root and determining its approximate asymptotic distribution. In this paper, however, we concentrate on the exact distribution of the roots as a random set. In particular, we propose the use of higher-order root intensity functions as a tool for examining the properties of the roots and determining their most problematic features. The use of root intensity functions of first and second order is illustrated by application to the score function for the Cauchy location model.     

SPC—a team effort for process improvement across four Area Control Centres

This paper describes an innovative application of statistical process control to the online remote control of the UK's gas transportation networks. The gas industry went through a number of changes in ownership, regulation, access to networks, organization and management culture in the 1990s. The application of SPC was motivated by these changes along with the desire to apply the best industrial statistics theory to practical problems. The work was initiated by a studentship, with the technology gradually being transferred to the industry. The combined efforts of control engineers and statisticians helped develop a novel SPC system. Having set up the control limits, a system was devised to automatically update and publish the control charts on a daily basis. The charts and an associated discussion forum are available to both managers and control engineers throughout the country at their desktop PCs. The paper describes methods of involving people to design first-class systems to achieve continual process improvement. It describes how the traditional benefits of SPC can be realized in a 'distal team working', and 'soft systems', context of four Area Control Centres, controlling a system delivering two thirds of the UK's energy needs.     

Predicting distances using a linear model: the case of varietal distinctness

Differences between plant varieties are based on phenotypic observations, which are both space and time consuming. Moreover, the phenotypic data result from the combined effects of genotype and environment. On the contrary, molecular data are easier to obtain and give a direct access to the genotype. In order to save experimental trials and to concentrate efforts on the relevant comparisons between varieties, the relationship between phenotypic and genetic distances is studied. It appears that the classical genetic distances based on molecular data are not appropriate for predicting phenotypic distances. In the linear model framework, we define a new pseudo genetic distance, which is a prediction of the phenotypic one. The distribution of this distance given the pseudo genetic distance is established. Statistical properties of the predicted distance are derived when the parameters of the model are either given or estimated. We finally apply these results to distinguishing between 144 maize lines. This case study is very satisfactory because the use of anonymous molecular markers (RFLP) leads to saving 29% of the trials with an acceptable error risk. These results need to be confirmed on other varieties and species and would certainly be improved by using genes coding for phenotypic traits.     

Boosting with Bayesian stumps

Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for performing boosting and show how they can be fit into a Bayesian setup at each iteration of the algorithm. We demonstrate how this formulation gives rise to a new splitting criterion when using a domain-partitioning classification method such as a decision tree. Further we can improve the predictive performance of simple decision trees, known as stumps, by using a posterior weighted average of them to classify at each step of the algorithm, rather than just a single stump. The main advantage of this approach is to reduce the number of boosting iterations required to produce a good classifier with only a minimal increase in the computational complexity of the algorithm.     

Non-parametric Estimation of the Residual Distribution

Consider a heteroscedastic regression model Y = m ( X ) +σ( X )ε, where the functions m and σ are smooth, and ε is independent of X . An estimator of the distribution of ε based on non-parametric regression residuals is proposed and its weak convergence is obtained. Applications to prediction intervals and goodness-of-fit tests are discussed.