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Background: Federal regulations require that health care organizations provide language services to patients with limited English proficiency. The National Standards for Culturally and Linguistically Appropriate Services in Health Care (CLAS standards) provide guidance on how to fulfill these regulations. It is not known how US hospitals have incorporated them into practice. Objectives: To assess how US hospitals are meeting federal regulations requiring provision of language services using CLAS as a measure of compliance. Research Design: Cross-sectional survey. Subjects: Hospital interpreter services managers (or equivalent position). Measures: Degree of meeting each of the 4 language-related CLAS standards. Results: Many hospitals are not meeting federal regulations. The majority reported providing language assistance in a timely manner in their first, but not their third, most commonly requested language. Although hospitals reported that they informed patients of their right to receive language services, many did so only in English. A majority of hospitals reported the use of family members or untrained staff as interpreters. Few reported providing vital documents in non-English languages. Overall, 13% of hospitals met all 4 of the language-related CLAS standards, whereas 19% met none. Conclusions: Our study documents that many hospitals are not providing language services in a manner consistent with federal law. Enforcement of these regulations is inconsistent, and thus does not motivate hospitals to comply. Compliance will likely come with new guidelines, currently being written, by many of the regulatory organizations. Our study reinforces the importance of these efforts and helps target interventions to improve the delivery and safety of care to limited English proficient patients.

Chronograms from molecular dating are increasingly being used to infer rates of diversification and their change over time. A major limitation in such analyses is incomplete species sampling that moreover is usually nonrandom. While the widely used γ statistic with the Monte Carlo constant-rates test or the birth-death likelihood analysis with the AAICrc test statistic are appropriate for comparing the fit of different diversification models in phylogenies with random species sampling, no objective automated method has been developed for fitting diversification models to nonrandomly sampled phylogenies. Here, we introduce a novel approach, CorSiM, which involves simulating missing splits under a constant rate birth-death model and allows the user to specify whether species sampling in the phylogeny being analyzed is random or nonrandom. The completed trees can be used in subsequent model-fitting analyses. This is fundamentally different from previous diversification rate estimation methods, which were based on null distributions derived from the incomplete trees. CorSiM is automated in an R package and can easily be applied to large data sets. We illustrate the approach in two Araceae clades, one with a random species sampling of 52% and one with a nonrandom sampling of 55%. In the latter clade, the CorSiM approach detects and quantifies an increase in diversification rate, whereas classic approaches prefer a constant rate model; in the former clade, results do not differ among methods (as indeed expected since the classic approaches are valid only for randomly sampled phylogenies). The CorSiM method greatly reduces the type I error in diversification analysis, but type II error remains a methodological problem.

A common pattern found in phylogeny-based empirical studies of diversification is a decrease in the rate of lineage accumulation toward the present. This early-burst pattern of cladogenesis is often interpreted as a signal of adaptive radiation or density-dependent processes of diversification. However, incomplete taxonomie sampling is also known to artifactually produce patterns of rapid initial diversification. The Monte Carlo constant rates (MCCR) test, based upon Pybus and Harvey's gamma (γ)-statistic, is commonly used to accommodate incomplete sampling, but this test assumes that missing taxa have been randomly pruned from the phylogeny. Here we use simulations to show that preferentially sampling disparate lineages within a clade can produce severely inflated type-I error rates of the MCCR test, especially when taxon sampling drops below 75%. We first propose two corrections for the standard MCCR test, the proportionally deeper splits that assumes missing taxa are more likely to be recently diverged, and the deepest splits only MCCR that assumes that all missing taxa are the youngest lineages in the clade, and assess their statistical properties. We then extend these two tests into a generalized form that allows the degree of nonrandom sampling (NRS) to be controlled by a scaling parameter, α. This generalized test is then applied to two recent studies. This new test allows systematists to account for nonrandom taxonomie sampling when assessing temporal patterns of lineage diversification in empirical trees. Given the dramatic affect NRS can have on the behavior of the MCCR test, we argue that evaluating the sensitivity of this test to NRS should become the norm when investigating patterns of cladogenesis in incompletely sampled phylogenies.

We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness-adjusted t-statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t-statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous.

In the absence of true regional data on changes in the acid/base status of lakes in the northeastern United States, we explore the possibility of using site-specific trends information from a judgment sample of lakes to assess the efficacy of the Clean Air Act Amendments. A meta-analytical technique is used to combine trends results from 44 Long-Term Monitoring (LTM) lakes in the Northeast for the period 1982-1994, with the goal of producing estimates of overall trends in the region. The lakes are subdivided into subpopulations (High ANC, Intermediate Till Drainage, Thin Till Drainage and Perched Seepage lakes) on the basis of their expected response to changes in acidic deposition, and they appear to represent the most acid-sensitive of these lake classes well. While the overall tendencies in the trends are as expected (e.g., most of the recovery is observed in the most sensitive subpopulations), there is significant trend heterogeneity among the lakes within most of the subpopulations; this heterogeneity prohibits the summarizing of trends at the regional level (i.e., for all of the Northeast). This heterogeneity is explained by differences in the responses of lakes in two subregions (Adirondacks vs. New England), and we present trends results separately for each subpopulation within these two subregions. All subpopulations in both subregions showed decreasing trends in sulfate concentrations, probably a reflection of decreasing trends in sulfur deposition in the region. Few trends in nitrate concentrations were observed. Recovery (as evidenced by increasing trends in acid-neutralizing capacity) was evident in Thin Till and Intermediate Till Drainage lakes in New England, but not in the Adirondacks. Most groups of lakes exhibited downward trends in base cations (∑[Ca2++ Mg2++ Na++ K+]); the magnitudes of these trends were always greater in Adirondack lakes than in similar New England lakes. This suggests that the depletion of soil cation pools in the Adirondacks may be responsible for some of the differences in recovery between Adirondack and New England lakes. While export of base cations may be the key difference producing different trends results in the two subregions, the site-specific nature of the trends, and their possible lack of regional representation, should be considered in interpreting the overall results.

A distribution-free procedure is developed to test a stochastic order relation between two distributions based on judgment post-stratified (JPS) data. The proposed inference relies on Mann-Whitney rank sum statistics. A first class of tests constructs test statistics by comparing all units in both samples, while a second class first stratifies the data into judgment classes and then constructs a rank sum statistic in each stratum, with the final test statistic constructed from a linear combination of these within-judgment class rank sum statistics. Distributional properties of the testing procedures are investigated. The null distributions of the test statistics in the first class depend on the quality of ranking information while the null distributions of the test statistics in the second class are distribution-free for any sample sizes, regardless of the quality of ranking information. Both tests have higher efficiencies than corresponding tests based on a simple random sample rank sum statistic. For large samples, testing procedures in the first and second classes are equivalent, respectively, to Bohn-Wolfe and Fligner-MacEachern testing procures in a ranked set sampling design.

We develop an unbiased estimator of the variance of a population based on a ranked set sample. We show that this new estimator is better than estimating the variance based on a simple random sample and more efficient than the estimator based on a ranked set sample proposed by Stokes. Also, a test to determine the effectiveness of the judgment ordering process is proposed.

The ranked-set sampling literature includes both inference procedures that rely on the assumption of perfect rankings and inference procedures that are robust to violations of this assumption. Procedures that assume perfect rankings tend to be more efficient when rankings are in fact perfect, but they may be invalid when perfect rankings fail. As a result, users of ranked-set sampling must decide between efficiency and robustness, and there is at present little to guide their decision. In this article we introduce three rank-based goodness-of-fit tests that may be consulted in making these decisions. Our strategy in producing these tests is to think of the judgment order statistic classes as separate samples, compute the ranks of the units from each sample within the combined sample, and use these ranks to test whether the judgment rankings are perfect. Consideration of both power and ease of use leads us to recommend use of a test that rejects when the concordance between the vector of mean ranks and its null expectation is small. Tables of critical values and appropriate asymptotic theory for applying this test are provided, and we illustrate the use of the tests by applying them to a biological dataset.

Let (Bn) be an increasing sequence of regions in d-dimensional space with volume n and with union Rd. We prove a general central limit theorem for functionals of point sets, obtained either by restricting a homogeneous Poisson process to Bn, or by by taking n uniformly distributed points in Bn. The sets Bncould be all cubes but a more general class of regions Bnis considered. Using this general result we obtain central limit theorems for specific functionals such as total edge length and number of components, defined in terms of graphs such as the k-nearest neighbors graph, the sphere of influence graph and the Voronoi graph.

MacEachern, Stasny, and Wolfe (2004, Biometrics 60, 207-215) introduced a data collection method, called judgment poststratification (JPS), based on ideas similar to those in ranked set sampling, and proposed methods for mean estimation from JPS samples. In this article, we propose an improvement to their methods, which exploits the fact that the distributions of the judgment poststrata are often stochastically ordered, so as to form a mean estimator using isotonized sample means of the poststrata. This new estimator is strongly consistent with similar asymptotic properties to those in MacEachern et al. (2004). It is shown to be more efficient for small sample sizes, which appears to be attractive in applications requiring cost efficiency. Further, we extend our method to JPS samples with imprecise ranking or multiple rankers. The performance of the proposed estimators is examined on three data examples through simulation. /// MacEachern, Stasny et Wolfe ont présenté, en 2004, une nouvelle méthode de recueil de données appelée jugement post-stratification (JP-S) et inspirée des mêmes idées que l'échantillonnage par ensembles avec classement (RSS: \"ranked set sampling\"); ils ont également présenté des méthodes d'estimation de la moyenne à partir de tels échantillonnages. Dans cet article, nous proposons d'améliorer leurs méthodes en exploitant le fait que les distributions du jugement dans les différentes strates constituées a posteriori sont souvent ordonnées de façon stochastique: il s'agit alors de construire un estimateur de la moyenne à partir des estimateurs isotoniques propres à chaque strate. Ce nouvel estimateur est tout-à-fait cohérent avec celui de MacEachern et al. (2004), et présente des propriétés asymptotiques semblables. Il se révèle plus efficace que ce dernier dans les échantillons de petite taille, lorsque la problématique du coût de l'étude est majeure. Qui plus est, nous adaptons notre estimateur aux échantillons avec JP-S où les rangs sont attribués de façon imprécise ou bien par plusieurs personnes. La performance des estimateurs ainsi proposés est étudiée par simulation sur trois jeux de données.