Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

An empirical examination of cost-adjusting in outpatient clinics

This paper presents an empirical methodology for examining cost-adjusting in relation to multiple-output health care providers. The methodology is subsequently implemented using a sample of California outpatient clinics. The empirical evidence is that these clinics do cost-adjust; that is, the clinics respond to low Medicare reimbursement by using lower quality to control the marginal costs of those patient groups not insured under a government-sponsored plan. In addition, clinics do not cost-adjust with respect to Medi-Cal patients, implying that various government reimbursement systems have different effects on a provider’s cost-adjusting behavior.     

An Empirical Examination of Quality Certification in a "Lemons Market"

This article examines the effect third-party certification has on a market characterized by adverse selection. Using an original data set from the market for young thoroughbreds, we show that certification alleviates problems of adverse selection by examining the effect certification has on breeder decisions to retain or sell horses and the effect these decisions have on observed prices. Data on the racetrack performance of the horses confirm the results.     

A simple estimator for a shared frailty regression model

Summary. We propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution. Inferences for the frailty parameter can be obtained by using output from Cox regression analyses. The computational burden is substantially less than that of the other approaches to estimation. The large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes. The methods are motivated by studies of familial associations in the natural history of diseases. Their practical utility is illustrated with sib pair data from Beaver Dam, Wisconsin.     

A non-profit approach to addressing the effects of urbanization in the Lake Pontchartrain Basin, Louisiana

The Lake Pontchartrain Basin in southeast Louisiana is an estuarine watershed encompassing New Orleans, Baton Rouge, and wetlands that are an integral part of Louisiana's unique culture. In recent decades rapid growth and poorly planned development have resulted in a decrease in water quality and habitat loss, especially in St. Tammany Parish on the north shore of Lake Pontchartrain. In 1989, the Lake Pontchartrain Basin Foundation (LPBF), an environmental, non-profit organization, was formed to address and rectify environmental issues in the Basin. LPBF accomplishes its mission through programs addressing water quality, habitat protection, geographic information science, public access, and education.LPBF used the recent, rapid development occurring in St. Tammany Parish as a case study to examine the relationship between land use, water quality, and development in the Basin by investigating habitat change/urbanization, development in flood plains, and water quality. Through the integration of LPBF programs, it was found that urbanization is having a devastating impact on the parish's natural habitats and severely degrading water quality. Using these results, LPBF is educating Basin citizens on the protection of their habitats and water quality to insure that these resources may be available for future generations.