Loss Function Based Ranking in Two-Stage, Hierarchical Models

Several authors have studied the performance of optimal, squared error loss (SEL) estimated ranks. Though these are effective, in many applications interest focuses on identifying the relatively good (e.g., in the upper 10%) or relatively poor performers. We construct loss functions that address this goal and evaluate candidate rank estimates, some of which optimize specific loss functions. We study performance for a fully parametric hierarchical model with a Gaussian prior and Gaussian sampling distributions, evaluating performance for several loss functions. Results show that though SEL-optimal ranks and percentiles do not specifically focus on classifying with respect to a percentile cut point, they perform very well over a broad range of loss functions. We compare inferences produced by the candidate estimates using data from The Community Tracking Study

Ranking USRDS Provider-Specific SMRs from 1998-2001

Provider profiling (ranking, league tables ) is prevalent in health services research. Similarly, comparing educational institutions and identifying differentially expressed genes depend on ranking. Effective ranking procedures must be structured by a hierarchical (Bayesian) model and guided by a ranking-specific loss function, however even optimal methods can perform poorly and estimates must be accompanied by uncertainty assessments. We use the 1998-2001 Standardized Mortality Ratio (SMR) data from United States Renal Data System (USRDS) as a platform to identify issues and approaches. Our analyses extend Liu et al. (2004) by combining evidence over multiple years via an AR(1) model; by considering estimates that minimize errors in classifying providers above or below a percentile cutpoint in addition to those that minimize rank-based, squared-error loss; by considering ranks based on the posterior probability that a provider\u27s SMR exceeds a threshold; by comparing these ranks to those produced by ranking MLEs and ranking P-values associated with testing whether a provider\u27s SMR = 1; by comparing results for a parametric and a non-parametric prior; by reporting on a suite of uncertainty measures. Results show that MLE-based and hypothesis test based ranks are far from optimal, that uncertainty measures effectively calibrate performance; that in the USRDS context ranks based on single-year data perform poorly, but that performance improves substantially when using the AR(1) model; that ranks based on posterior probabilities of exceeding a properly chosen SMR threshold are essentially identical to those produced by minimizing classification loss. These findings highlight areas requiring additional research and the need to educate stakeholders on the uses and abuses of ranks; on their proper role in science and policy; on the absolute necessity of accompanying estimated ranks with uncertainty assessments and ensuring that these uncertainties influence decisions

The Health Effects of Medicare for the Near-Elderly Uninsured

We study how the trajectory of health for the near-elderly uninsured changes upon enrolling into Medicare at the age of 65. We find that Medicare increases the probability of the previously uninsured having excellent or very good health, decreases their probability of being in good health, and has no discernable effects at lower health levels. Surprisingly, we found Medicare had a similar effect on health for the previously insured. This suggests that Medicare helps the relatively healthy 65 year olds, but does little for those who are already in declining health once they reach the age of 65. The improvement in health between the uninsured and insured were not statistically different from each other. The stability of insurance coverage afforded by Medicare may be the source of the health benefit suggesting that universal coverage at other ages may have similar health effects.

Why the DEA STRIDE data are still useful for understanding drug markets

In 2001, use of the STRIDE data base for the purpose of analyzing drug prices and the impact of public policies on drug markets came under serious attack by the National Research Council (Manski, et al., 2001; Horowitz, 2001). While some of the criticisms raised by the committee were valid, many of the concerns can be easily addressed through more careful use of the data. In this paper, we first disprove Horowitz's main argument that prices are different for observations collected by different agencies within a city. We then revisit other issues raised by the NRC and discuss how certain limitations can be easily overcome through the adoption of random coefficient models of drug prices and by paying serious attention to drug form and distribution levels. Although the sample remains a convenience sample, we demonstrate how construction of city-specific price and purity series that pay careful attention to the data and incorporate existing knowledge of drug markets (e.g. the expected purity hypothesis) are internally consistent and can be externally validated. The findings from this study have important implications regarding the utility of these data and the appropriateness of using them in econmic analyses of supply, demand and harms.Approved for public release; distribution is unlimited

Why the DEA STRIDE Data are Still Useful for Understanding Drug Markets

In 2001, use of the STRIDE data base for the purposes of analyzing drug prices and the impact of public policies on drug markets came under serious attack by the National Research Council (Manski et al., 2001; Horowitz, 2001). While some of the criticisms raised by the committee were valid, many of the concerns can be easily addressed through more careful use of the data. In this paper, we first disprove Horowitz's main argument that prices are different for observations collected by different agencies within a city. We then revisit other issues raised by the NRC and discuss how certain limitations can be easily overcome through the adoption of random coefficient models of drug prices and by paying serious attention to drug form and distribution levels. Although the sample remains a convenience sample, we demonstrate how construction of city-specific price and purity series that pay careful attention to the data and incorporate existing knowledge of drug markets (e.g. the expected purity hypothesis) are internally consistent and can be externally validated. The findings from this study have important implications regarding the utility of these data and the appropriateness of using them in economic analyses of supply, demand and harms.

Ehrlichia ewingii Infection in White-Tailed Deer (Odocoileus virginianus)

Two closely related zoonotic ehrlichiae, Ehrlichia chaffeensis and E. ewingii, are transmitted by Amblyomma americanum, the lone star tick. Because white-tailed deer (Odocoileus virginianus) are critical hosts for all mobile stages of A. americanum and are important vertebrate reservoirs of E. chaffeensis, we investigated whether deer may be infected with E. ewingii, a cause of granulocytotropic ehrlichiosis in humans and dogs. To test for E. ewingii infection, we used polymerase chain reaction and inoculation of fawns with whole blood from wild deer. Of 110 deer tested from 20 locations in 8 U.S. states, 6 (5.5%) were positive for E. ewingii. In addition, natural E. ewingii infection was confirmed through infection of captive fawns. These findings expand the geographic distribution of E. ewingii, along with risk for human infection, to include areas of Kentucky, Georgia, and South Carolina. These data suggest that white-tailed deer may be an important reservoir for E. ewingii