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Regression Models (regression + models)
Kinds of Regression Models Selected AbstractsBOOTSTRAP TESTS FOR THE ERROR DISTRIBUTION IN LINEAR AND NONPARAMETRIC REGRESSION MODELSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2006Natalie Neumeyer Summary In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and non-parametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the resulting bootstrap test is investigated from an asymptotic point of view and by means of a simulation study. The results demonstrate that even for moderate sample sizes the parametric bootstrap provides a reliable and easy accessible solution to the problem of goodness-of-fit testing of assumptions regarding the error distribution in linear and non-parametric regression models. [source] Identification and Estimation of Regression Models with MisclassificationECONOMETRICA, Issue 3 2006Aprajit Mahajan This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric regression functions follows straightforwardly from the basic identification result. We propose a kernel estimator based on the identification strategy, derive its large sample properties, and discuss alternative estimation procedures. We also propose a test for misclassification in the model based on an exclusion restriction that is straightforward to implement. [source] Comparison of Linear Regression Models for Quantitative Geochemical Analysis: An Example Using X-Ray Fluorescence SpectrometryGEOSTANDARDS & GEOANALYTICAL RESEARCH, Issue 3 2005Mirna Guevara calibration analytique; régression linéaire; matériaux de référence en géochimie; géochimie analytique; loi de propagation d'erreurs This paper presents statistical aspects related to the calibration process and a comparison of different regression approaches of relevance to almost all analytical techniques. The models for ordinary least-squares (OLS), weighted least-squares (WLS), and maximum likelihood fitting (MLF) were evaluated and, as a case study, X-ray fluorescence (XRF) calibration curves for major elements in geochemical reference materials were used. The results showed that WLS and MLF models were statistically more consistent in comparison with the usually applied OLS approach. The use of uncertainty on independent and dependent variables during the calibration process and the calculation of final uncertainty on individual results using error propagation equations are the novel aspects of our work. Cet article présente les aspects statistiques liés au processus de calibration et fait une comparaison des différents calculs de régression utilisés dans pratiquement toutes les techniques analytiques. Les modèles des moindres carrés ordinaires (MCO) et pondérés (MCP), et d'ajustement de maximum de vraisemblance (AMV) ont étéévalués et appliqués aux courbes de calibration d'éléments majeurs obtenues en analyse par fluorescence X (XRF) de matériaux certifiés de référence. Les résultats obtenus avec les modèles MCP et AMV sont plus cohérents statistiquement que ceux obtenus la méthode classique des MCO. L'utilisation de l'incertitudes sur des variables indépendantes ou dépendantes durant la procédure de calibration et le calcul de l'incertitude finale sur chaque résultat à partir des lois de propagation d'erreur sont des aspects novateurs de ce travail. [source] A revisit on older adults suicides and Severe Acute Respiratory Syndrome (SARS) epidemic in Hong KongINTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 12 2008Y. T. Cheung Abstract Background The Severe Acute Respiratory Syndrome (SARS) outbreak in 2003 had an enormous impact on Hong Kong society and the suicide rate was also at its historical high, 18.6 per 100,000. The most significant increase was found among the older adults aged 65 or above. Methods Poisson Regression Models were used to examine impact of the SARS epidemic on older adults suicides in Hong Kong. A complete set of the suicide statistics for the period 1993,2004 from the Coroners' Court were made available for the analysis. Chi-square test was used to compare the profile of the older adult suicide cases in the pre-SARS, peri-SARS and post-SARS periods. Results It showed an excess of older adults suicides in April 2003, when compared to the month of April of the other years. A trough, instead of the usual summer peak, was observed in June, suggesting some of the older adults suicides might have been brought forward. On a year basis, the annual older adult's suicide rates in 2003 and 2004 were significantly higher than that in 2002, suggesting the suicide rate did not return to the level before the SARS epidemic. Based on the Coroners' suicide death records, overall severity of illness, level of dependency and worrying of having sickness among the older adult suicides were found to be significantly different in the pre-SARS, peri-SARS and post-SARS periods. Conclusion The SARS epidemic was associated with an increase in older adults' suicide rate in April 2003 and some suicide deaths in June 2003 might have been brought forward. Moreover, an increase in the annual older adults' suicide rate in 2003 was observed and the rate in 2004 did not return to the level of 2002. Loneliness and disconnectedness among the older adults in the community were likely to be associated with the excess older adults' suicides in 2003. Maintaining and enhancing mental well being of the public over the period of epidemic is as important as curbing the spread of the epidemic. Attention and effort should also be made to enhance the community's ability to manage fear and anxiety, especially in vulnerable groups over the period of epidemic to prevent tragic and unnecessary suicide deaths. Copyright © 2008 John Wiley & Sons, Ltd. [source] Elderly suicide and the 2003 SARS epidemic in Hong KongINTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 2 2006Sau Man Sandra Chan Abstract Background Hong Kong was struck by the community outbreak of Severe Acute Respiratory Syndrome (SARS) in 2003. In the same year, the elderly suicide rate in Hong Kong showed a sharp upturn from a previous downward trend. Methods Secondary analyses using Poisson Regression Models on the suicide statistics from the Census and Statistics Department of the Hong Kong Government were performed. Results In a Poisson Regression Model on the annual suicide rates in elders aged 65 and over in years 1986,2003, 2002 served as the reference year. Suicide rates in 1986,1997 were significantly higher than the reference year, with an Incident Rate Ratio (IRR) of 1.34 to 1.61. However, rates in 1998,2001 did not differ from the reference year significantly, representing stabilization of suicide rates for 4 years after 1997. The elderly suicide rate increased to 37.46/100,000 in 2003, with an IRR of 1.32 (p,=,0.0019) relative to 2002. Such trend is preserved when female elderly suicide rates in 1993,2003 were analyzed, while suicide rates in elderly men and younger age groups did not follow this pattern. Discussions Mechanistic factors such as breakdown of social network and limited access to health care might account for the findings. These factors could have potentiated biopsychosocial risk factors for suicide at individual levels, particularly in elderly. Female elders, by way of their previous readiness to utilize social and health services instituted in the past decade, are thus more susceptible to the effects of temporary suspension of these services during the SARS epidemic. Conclusions The SARS epidemic was associated with increased risk of completed suicide in female elders, but not in male elders or the population under 65 years of age. Copyright © 2006 John Wiley & Sons, Ltd. [source] Confidence Intervals in Generalized Regression Models by Esa UusipaikkaINTERNATIONAL STATISTICAL REVIEW, Issue 1 2009Erkki P. Liski No abstract is available for this article. [source] A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and NonlinearityJOURNAL OF REGIONAL SCIENCE, Issue 2 2001Thomas De Graaff There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory,the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio. [source] Confidence Intervals in Generalized Regression ModelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 3 2010L. T. Skovgaard No abstract is available for this article. [source] Default Bayesian Priors for Regression Models with First-Order Autoregressive ResidualsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2003Malay Ghosh Abstract. The objective of this paper is to develop default priors when the parameter of interest is the autocorrelation coefficient in normal regression models with first-order autoregressive residuals. Jeffreys' prior as well as reference priors are found. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys' prior in this respect. Also, the credible intervals based on these reference priors seem superior to similar intervals based on certain divergence measures. [source] Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 5 2010Virmantas Kvedaras Abstract An increasing variety of data frequencies available in economics, finance, etc. gives rise to a question how to build and estimate a regression model with variables observed at different frequencies. In a unifying framework of (m,d)-aggregation we consider various approaches by discussing some potential and limitations. A Monte Carlo experiment and an empirical example illustrate that the traditional fixed aggregation approach, widely used in applied economics, might be inconsistent with data and highly inferior in terms of model precision. [source] Logistic Regression Models for Polymorphic and Antagonistic Pleiotropic Gene Action on Human Aging and LongevityANNALS OF HUMAN GENETICS, Issue 6 2003Qihua Tan Summary In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture the age-dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age-dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important gene variations that contribute to human aging and longevity. [source] Likelihood Methods for Regression Models with Expensive Variables Missing by DesignBIOMETRICAL JOURNAL, Issue 1 2009Yang Zhao Abstract In some applications involving regression the values of certain variables are missing by design for some individuals. For example, in two-stage studies (Zhao and Lipsitz, 1992), data on "cheaper" variables are collected on a random sample of individuals in stage I, and then "expensive" variables are measured for a subsample of these in stage II. So the "expensive" variables are missing by design at stage I. Both estimating function and likelihood methods have been proposed for cases where either covariates or responses are missing. We extend the semiparametric maximum likelihood (SPML) method for missing covariate problems (e.g. Chen, 2004; Ibrahim et al., 2005; Zhang and Rockette, 2005, 2007) to deal with more general cases where covariates and/or responses are missing by design, and show that profile likelihood ratio tests and interval estimation are easily implemented. Simulation studies are provided to examine the performance of the likelihood methods and to compare their efficiencies with estimating function methods for problems involving (a) a missing covariate and (b) a missing response variable. We illustrate the ease of implementation of SPML and demonstrate its high efficiency (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified ModelsBIOMETRICS, Issue 3 2009Michael Rosenblum Summary Regression models are often used to test for cause-effect relationships from data collected in randomized trials or experiments. This practice has deservedly come under heavy scrutiny, because commonly used models such as linear and logistic regression will often not capture the actual relationships between variables, and incorrectly specified models potentially lead to incorrect conclusions. In this article, we focus on hypothesis tests of whether the treatment given in a randomized trial has any effect on the mean of the primary outcome, within strata of baseline variables such as age, sex, and health status. Our primary concern is ensuring that such hypothesis tests have correct type I error for large samples. Our main result is that for a surprisingly large class of commonly used regression models, standard regression-based hypothesis tests (but using robust variance estimators) are guaranteed to have correct type I error for large samples, even when the models are incorrectly specified. To the best of our knowledge, this robustness of such model-based hypothesis tests to incorrectly specified models was previously unknown for Poisson regression models and for other commonly used models we consider. Our results have practical implications for understanding the reliability of commonly used, model-based tests for analyzing randomized trials. [source] Median Regression Models for Longitudinal Data with DropoutsBIOMETRICS, Issue 2 2009Grace Y. Yi Summary Recently, median regression models have received increasing attention. When continuous responses follow a distribution that is quite different from a normal distribution, usual mean regression models may fail to produce efficient estimators whereas median regression models may perform satisfactorily. In this article, we discuss using median regression models to deal with longitudinal data with dropouts. Weighted estimating equations are proposed to estimate the median regression parameters for incomplete longitudinal data, where the weights are determined by modeling the dropout process. Consistency and the asymptotic distribution of the resultant estimators are established. The proposed method is used to analyze a longitudinal data set arising from a controlled trial of HIV disease (Volberding et al., 1990, The New England Journal of Medicine322, 941,949). Simulation studies are conducted to assess the performance of the proposed method under various situations. An extension to estimation of the association parameters is outlined. [source] Variable Selection and Model Choice in Geoadditive Regression ModelsBIOMETRICS, Issue 2 2009Thomas Kneib Summary Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection. [source] Polynomial Spline Estimation and Inference of Proportional Hazards Regression Models with Flexible Relative Risk FormBIOMETRICS, Issue 3 2006Jianhua Z. Huang Summary The Cox proportional hazards model usually assumes an exponential form for the dependence of the hazard function on covariate variables. However, in practice this assumption may be violated and other relative risk forms may be more appropriate. In this article, we consider the proportional hazards model with an unknown relative risk form. Issues in model interpretation are addressed. We propose a method to estimate the relative risk form and the regression parameters simultaneously by first approximating the logarithm of the relative risk form by a spline, and then employing the maximum partial likelihood estimation. An iterative alternating optimization procedure is developed for efficient implementation. Statistical inference of the regression coefficients and of the relative risk form based on parametric asymptotic theory is discussed. The proposed methods are illustrated using simulation and an application to the Veteran's Administration lung cancer data. [source] Assessment of Agreement under Nonstandard Conditions Using Regression Models for Mean and VarianceBIOMETRICS, Issue 1 2006Pankaj K. Choudhary Summary The total deviation index of Lin (2000, Statistics in Medicine19, 255,270) and Lin et al. (2002, Journal of the American Statistical Association97, 257,270) is an intuitive approach for the assessment of agreement between two methods of measurement. It assumes that the differences of the paired measurements are a random sample from a normal distribution and works essentially by constructing a probability content tolerance interval for this distribution. We generalize this approach to the case when differences may not have identical distributions,a common scenario in applications. In particular, we use the regression approach to model the mean and the variance of differences as functions of observed values of the average of the paired measurements, and describe two methods based on asymptotic theory of maximum likelihood estimators for constructing a simultaneous probability content tolerance band. The first method uses bootstrap to approximate the critical point and the second method is an analytical approximation. Simulation shows that the first method works well for sample sizes as small as 30 and the second method is preferable for large sample sizes. We also extend the methodology for the case when the mean function is modeled using penalized splines via a mixed model representation. Two real data applications are presented. [source] Imputation and Variable Selection in Linear Regression Models with Missing CovariatesBIOMETRICS, Issue 2 2005Xiaowei Yang Summary Across multiply imputed data sets, variable selection methods such as stepwise regression and other criterion-based strategies that include or exclude particular variables typically result in models with different selected predictors, thus presenting a problem for combining the results from separate complete-data analyses. Here, drawing on a Bayesian framework, we propose two alternative strategies to address the problem of choosing among linear regression models when there are missing covariates. One approach, which we call "impute, then select" (ITS) involves initially performing multiple imputation and then applying Bayesian variable selection to the multiply imputed data sets. A second strategy is to conduct Bayesian variable selection and missing data imputation simultaneously within one Gibbs sampling process, which we call "simultaneously impute and select" (SIAS). The methods are implemented and evaluated using the Bayesian procedure known as stochastic search variable selection for multivariate normal data sets, but both strategies offer general frameworks within which different Bayesian variable selection algorithms could be used for other types of data sets. A study of mental health services utilization among children in foster care programs is used to illustrate the techniques. Simulation studies show that both ITS and SIAS outperform complete-case analysis with stepwise variable selection and that SIAS slightly outperforms ITS. [source] A latitudinal gradient of beta diversity for exotic vascular plant species in North AmericaDIVERSITY AND DISTRIBUTIONS, Issue 3 2008Hong Qian ABSTRACT Determining relationships between the ranges of introduced species and geographical and environmental factors is an important step in understanding the mechanisms and processes of the spread of introduced species. In this study, I examined the beta diversity and latitude relationship for all naturalized exotic species of vascular plants in North America at a continental scale. Beta diversity was calculated as the absolute value of the slope of the relationship between the natural logarithm of the Simpson index of similarity (lnS) and spatial distance between pairs of state-level exotic floras within four latitudinal zones examined. Relative contributions of spatial distance and environmental difference to species turnover between exotic floras were examined. I found that beta diversity decreased monotonically from low to high latitudes: beta diversity for the southernmost zone was shallower than that for the northernmost zone by a factor of 2.6. Regression models of lnS in relation to spatial distance and environmental (climatic and topographical) difference for each latitudinal zone demonstrated that the explanatory power of these variables diminishes monotonically with latitude: the explained variance in lnS is 70.4%, 62.1%, 53.9%, and 33.9%, respectively, for the four latitudinal zones from south to north. For the southernmost zone, 58.3% of the variance in lnS is explained by climate variables and topography, and spatial distance explains only 2.3% of the variance. In contrast, for the northernmost zone, more than half the amount (22.5%) of the explained variance in lnS is attributable to spatial distance, and the remaining (18.9%) of the explained variance is attributable to climate variables and topography. [source] Uptake of inorganic chemicals from soil by plant leaves: Regressions of field data,ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 11 2001Rebecca A. Efroymson Abstract The estimation of chemical concentrations in wildlife foods, such as plant foliage, is often performed for risk assessments at contaminated sites. Regression models and uptake factors for use in estimating the uptake of inorganic elements from soil by above-ground plant tissues were derived in this study. These included models for arsenic, cadmium, copper, lead, mercury, nickel, selenium, and zinc. Models were developed using published data from soil contaminated in the field and were validated using measured concentrations from two contaminated sites. Single-variable regression models of log-transformed concentrations in plants versus log-transformed concentrations in soil are generally recommended over simple uptake factors for use in estimating plant uptake of inorganic contaminants in ecological risk assessments. Multiple regression models with soil concentration and pH as the variables are also recommended for estimating the uptake of four chemicals (cadmium, mercury, selenium, and zinc) by plants. Models for use in screening risk assessments, i.e., the upper 95% prediction limits on the regressions, are recommended to provide conservative estimates of uptake of inorganic chemicals by plants. [source] Tests of causal linkages between cannabis use and psychotic symptomsADDICTION, Issue 3 2005David M. Fergusson ABSTRACT Aim To examine possible causal linkages between cannabis use and psychosis using data gathered over the course of a 25-year longitudinal study. Design A 25-year longitudinal study of the health, development and adjustment of a birth cohort of 1265 New Zealand children (635 males, 630 females). Setting The Christchurch Health and Development Study, a general community sample. Participants A total of 1055 participants from the Christchurch Health and Development Study (CHDS) cohort for whom data on cannabis use and psychotic symptoms were available on at least one occasion from 18, 21 and 25 years. Measurements As part of this study, data were gathered on frequency of cannabis use and psychotic symptoms at ages 18, 21 and 25 years. Findings Regression models adjusting for observed and non-observed confounding suggested that daily users of cannabis had rates of psychotic symptoms that were between 1.6 and 1.8 times higher (P < 0.001) than non-users of cannabis. Structural equation modelling suggested that these associations reflected the effects of cannabis use on symptom levels rather than the effects of symptom levels on cannabis use. Conclusions The results of the present study add to a growing body of evidence suggesting that regular cannabis use may increase risks of psychosis. The present study suggests that: (a) the association between cannabis use and psychotic symptoms is unlikely to be due to confounding factors; and (b) the direction of causality is from cannabis use to psychotic symptoms. [source] The latitudinal gradient of beta diversity in relation to climate and topography for mammals in North AmericaGLOBAL ECOLOGY, Issue 1 2009Hong Qian ABSTRACT Aim Spatial turnover of species, or beta diversity, varies in relation to geographical distance and environmental conditions, as well as spatial scale. We evaluated the explanatory power of distance, climate and topography on beta diversity of mammalian faunas of North America in relation to latitude. Location North America north of Mexico. Methods The study area was divided into 313 equal-area quadrats (241 × 241 km). Faunal data for all continental mammals were compiled for these quadrats, which were divided among five latitudinal zones. These zones were comparable in terms of latitudinal and longitudinal span, climatic gradients and elevational gradients. We used the natural logarithm of the Jaccard index (lnJ) to measure species turnover between pairs of quadrats within each latitudinal zone. The slope of lnJ in relation to distance was compared among latitudinal zones. We used partial regression to partition the variance in lnJ into the components uniquely explained by distance and by environmental differences, as well as jointly by distance and environmental differences. Results Mammalian faunas of North America differ more from each other at lower latitudes than at higher latitudes. Regression models of lnJ in relation to distance, climatic difference and topographic difference for each zone demonstrated that these variables have high explanatory power that diminishes with latitude. Beta diversity is higher for zones with higher mean annual temperature, lower seasonality of temperature and greater topographic complexity. For each latitudinal zone, distance and environmental differences explain a greater proportion of the variance in lnJ than distance, climate or topography does separately. Main conclusions The latitudinal gradient in beta diversity of North American mammals corresponds to a macroclimatic gradient of decreasing mean annual temperature and increasing seasonality of temperature from south to north. Most of the variance in spatial turnover is explained by distance and environmental differences jointly rather than distance, climate or topography separately. The high predictive power of geographical distance, climatic conditions and topography on spatial turnover could result from the direct effects of physical limiting factors or from ecological and evolutionary processes that are also influenced by the geographical template. [source] Partitioning phylogenetic and adaptive components of the geographical body-size pattern of New World birdsGLOBAL ECOLOGY, Issue 1 2008Lizabeth Ramirez ABSTRACT Aim To evaluate seasonal body-size patterns for New World birds in geographical space, to develop environmental models to explain the gradients, and to estimate phylogenetic and adaptive contributions. Location The Western Hemisphere. Methods We used range maps to generate gridded geometric mean body masses. Summer and winter patterns were distinguished based on breeding and non-breeding ranges. We first generated the geographical gradients, followed by phylogenetic eigenvector regression to generate body sizes predicted by the birds' positions in a phylogenetic tree, which were used to generate the expected phylogenetic gradient. Subtracting the expected pattern from the observed pattern isolated the adaptive component. Ordinary least squares multiple-regression models examined factors influencing the phylogenetic, adaptive and combined components of the seasonal body-size patterns, and non-spatial and spatial models were compared. Results Birds are larger in the temperate zones than in the tropics. The gradient is quantitatively stronger in winter than in summer. Regression models explained 66.6% of the variance in summer mass and 45.9% of the variance in winter mass. In summer, phylogenetic and adaptive responses of birds contribute equally to the gradient. In winter, the gradient in North America is much stronger than that expected by taxonomic turnover, and responses of species independent of their family membership drive the overall pattern. Main conclusions We confirm Bergmann's rule in New World birds and conclude that winter temperatures ultimately drive the pattern, exerting selection pressures on birds that overwhelm patterns expected by phylogenetic inertia at the family level. However, in summer, the movement of migratory species into the temperate zone weakens the gradient and generates a pattern more congruent with that expected from the taxonomic composition of the fauna. The analytical method we develop here represents a useful tool for partitioning the phylogenetic and non-phylogenetic components of spatially explicit macroecological data. [source] Do Commercial Managed Care Members Rate Their Health Plans Differently than Medicaid Managed Care Members?HEALTH SERVICES RESEARCH, Issue 4 2003Patrick J. Roohan Objective. To determine if members of commercial managed care and Medicaid managed care rate the experience with their health plans differently. Data Sources. Data from both commercial and Medicaid Consumer Assessment of Health Plan Surveys (CAHPS) in New York State. Study Design. Regression models were used to determine the effect of population (commercial or Medicaid) on a member's rating of their health plan, controlling for health status, age, gender, education, race/ethnicity, number of office visits, and place of residence. Data Collection. Managed care plans are required to submit to the New York State Department of Health (NYSDOH) results of the annual commercial CAHPS survey. The NYSDOH conducted a survey of Medicaid enrollees using Medicaid CAHPS. Principal Findings. Medicaid managed care members in excellent or very good health rate their health plan higher than commercial members in excellent or very good health. There is no difference in health plan rating for commercial and Medicaid members in good, fair, or poor health. Older, less educated, black, and Hispanic members who live outside New York City are more likely to rate their managed care plan higher. Conclusions. Medicaid members rating of their health care equals or exceeds ratings by commercial members. [source] Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networksINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2001J.T. Schoof Abstract A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34,0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements. Copyright © 2001 Royal Meteorological Society [source] The physical environment influences neuropsychiatric symptoms and other outcomes in assisted living residentsINTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 10 2010Mark C. Bicket Abstract Objective Although the number of elderly residents living in assisted living (AL) facilities is rising, few studies have examined the AL physical environment and its impact on resident well-being. We sought to quantify the relationship of AL physical environment with resident outcomes including neuropsychiatric symptoms (NPS), quality of life (QOL), and fall risk, and to compare the effects for demented and non-demented residents. Methods Prospective cohort study of a stratified random sample of 326 AL residents living in 21 AL facilities. Measures included the Therapeutic Environmental Screening Scale for Nursing Homes and Residential Care (TESS-NH/RC) to rate facilities and in-person assessment of residents for diagnosis (and assessment of treatment) of dementia, ratings on standardized clinical, cognitive, and QOL measures. Regression models compared environmental measures with outcomes. TESS-NH/RC is modified into a scale for rating the AL physical environment AL-EQS. Results The AL Environmental Quality Score (AL-EQS) was strongly negatively associated with Neuropsychiatric Inventory (NPI) total score (p,<,0.001), positively associated with Alzheimer Disease Related Quality of Life (ADRQL) score (p,=,0.010), and negatively correlated with fall risk (p,=,0.042). Factor analysis revealed an excellent two-factor solution, Dignity and Sensory. Both were strongly associated with NPI and associated with ADRQL. Conclusion The physical environment of AL facilities likely affects NPS and QOL in AL residents, and the effect may be stronger for residents without dementia than for residents with dementia. Environmental manipulations that increase resident privacy, as well as implementing call buttons and telephones, may improve resident well-being. Copyright © 2010 John Wiley & Sons, Ltd. [source] Effects of climate on the growth of exotic and indigenous trees in central ZambiaJOURNAL OF BIOGEOGRAPHY, Issue 1 2005E. N. Chidumayo Abstract Aim, Climate change has far-reaching effects on species and ecosystems. The aims of this study were to determine how climate factors affect the growth pattern of indigenous and exotic trees in Zambia and to predict tree growth responses to a warmer climate with the use of mathematical models. Location, Two savanna sites in central Zambia. Methods, Diameter at breast height (1.3 m above ground, d.b.h.) of 91 permanently marked trees belonging to three indigenous and four exotic species was measured fortnightly for periods of 1,2 years from 1998 to 2003. Correlation and regression analysis was used to determine the effect of climate factors (minimum, maximum and average temperature and rainfall) on monthly daily d.b.h. increment of each species. Regression models were used to predict the growth behaviour of trees under a 0.5 °C warmer climate. Results, Interactions between temperature and rainfall explained 60,98% of the variation in d.b.h. increment in all the tree species, except the exotic Eucalyptus grandis. For deciduous species, stem expansion was delayed by 2,12 weeks following leaf-flush and d.b.h. increment peaked during the rainy season. Evergreen and deciduous species could not be separated on the basis of annual d.b.h. increment because the higher growth rates of deciduous species compensated for the shorter growing period. Mathematical models predicted slight changes in d.b.h. growth pattern under a 0.5 °C warmer climate in five of the seven species. Significant changes in d.b.h. growth patterns were predicted in the indigenous Bridelia micrantha and exotic Gmelina arborea under a warmer climate. However, models failed to adequately represent potential soil water stress that might result from changes in tree growth patterns and a warmer climate. Main conclusions, Climate factors explained a large proportion of the variation in diameter growth of both indigenous and exotic trees, rendering it possible to model tree growth patterns from climate data. Tree growth models suggest that a rise in temperature of 0.5 °C is unlikely to induce significant changes in the growth behaviour of the majority of the studied species. However, because the growth behaviour of some species may be substantially affected by climate change, it is recommended that strategies for the future production of such climate-sensitive trees should incorporate aspects of climate change. [source] The COMT val158met Polymorphism Is Associated With Peak BMD in Men,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 12 2004Mattias Lorentzon Abstract The associations between the functional val158met polymorphism of the estrogen-degrading COMT enzyme and skeletal properties in young men were investigated. BMD was associated with COMT genotype. Introduction: Peak BMD is an important predictor of future risk of osteoporosis, and it is to a large extent determined by genetic factors. Estrogens are involved in the accretion of bone mass during puberty. Catechol- O -methyltransferase (COMT) is involved in the degradation of estrogens. There is a functional polymorphism in the COMT gene (val158met), resulting in a 60,75% difference in enzyme activity between the val (high activity [H]) and met (low activity [L]) variants. The aim of this cross-sectional study was to investigate the associations between this polymorphism and peak BMD in young men. Materials and Methods: A total of 458 healthy men (mean age, 19 ± 0.6 years) were genotyped and classified as COMTLL, COMTHL, or COMTHH. Areal BMD (aBMD) was measured by DXA. Cortical and trabecular volumetric BMD (vBMD) were measured by pQCT. The associations between COMT genotype and skeletal phenotypes were determined. Results and Conclusions: Regression models using physical activity, height, weight, age, and COMT genotype as covariates showed that COMT genotype was an independent predictor of aBMD in the total body and in all femur locations investigated, but not in the spine. The values for COMTHL and COMTHH were very similar, and therefore, they were pooled into one group. aBMD at Ward's triangle, trochanter, and total femur were 4.9%, 4.5%, and 3.7% lower, respectively, in the COMTLL than in the COMTHL/HH group (p < 0.01). pQCT analyses showed that COMT genotype was an independent predictor of trabecular vBMD of the tibia, radius, and fibula. Trabecular vBMD of the radius and fibula in COMTLL was 5.3% and 7.4% lower, respectively, than that of the combined COMTHL/HH group. COMT genotype was associated with cortical vBMD but not with cortical cross-sectional area in the tibia. These findings show that the COMT polymorphism is associated with BMD in young adult men. [source] Does the organization of care processes affect outcomes in patients undergoing total joint replacement?JOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 1 2010Kris Vanhaecht RN MSc PhD Abstract Background, Surgeons realize that safe and efficient care processes for total joint replacement requires more than just well-performed operations. Orthopaedic teams are reorganizing care process to improve efficacy and shorten length of stay. Little is known on the impact of organizational changes on patient outcome. This paper studies the relation between the organization of care processes and patient outcomes in hip and knee. Clinical pathways are used as one of the methods to structure the care process. Although evidence is available on the effect of pathways in total joint replacement, their impact with the organization of the care process has not been studied previously. Methods, A cross-sectional multicentre study was performed on 39 care processes and 737 consecutive patients. Regression models were used to analyse the relation between the organization of the care process and risk-adjusted patient outcomes. The use of pathways and the organization of the care process, measured by the Care Process Self Evaluation Tool (CPSET), were measured at organizational level. Length of stay, pain, mobility and elapsed time to discharge were measured at patient level. Results, The use of pathways had a positive effect on four out of five subscales and the overall CPSET score. Using pathways decreased length of stay (P = 0.014), pain (P = 0.052) and elapsed time to discharge (P = 0.003). The CPSET subscale communication was related with three risk adjusted outcomes. Multivariate analysis demonstrated a significant effect by three different variables on the length of stay; (1) use of pathways; (2) coordination of care processes; and (3) communication with patients and family. Both the use of pathways and coordination of the care process were determinants for the elapsed time to discharge. A significant interaction effect was found between use of pathways and coordination of the care process. Conclusion, This large multicentre study revealed the relation between the use of pathways, organization of the care process and patient outcomes. This information is important for both clinicians and managers to understand and further improve the organization of orthopaedic care. Level of evidence, Level I prognostic study. [source] Latitudinal and altitudinal growth patterns of brown trout Salmo trutta at different spatial scalesJOURNAL OF FISH BIOLOGY, Issue 10 2009I. Parra Spatial variation in growth of stream-dwelling brown trout Salmo trutta was explored in 13 populations using a long-term study (1993,2004) in the Bay of Biscay drainage, northern Spain. The high variability in fork length (LF) of S. trutta in the study area was similar to the body-size range found in the entire European distribution of the species. Mean LF at age varied: 0+ years, 57·4,100·7 mm; 1+ years, 111·6,176·0 mm; 2+ years, 155·6,248·4 mm and 3+ years, 194·3,290·9 mm. Average LF at age was higher in main courses and lower reaches compared with small tributaries and upper reaches. Annual specific growth rates (GL) were: 0+ to 1+ years, 0·634,0·825 mm mm,1 year,1; 1+ to 2+ years, 0·243,0·342 mm mm,1 year,1; 2+ to 3+ years, 0·166,0·222 mm mm,1 year,1, showing a great homogeneity. Regression models showed that water temperature and altitude were the major determinants of LF at age variability within the study area. A broader spatial analysis using available data from stream-dwelling S. trutta populations throughout Europe indicated a negative relationship between latitude and LF of individuals and a negative interaction between latitude and altitude. These findings support previous evidence of the pervasive role of water temperature on the LF of this species. Altitude appeared as the overall factor that includes the local variation of other variables, such as water temperature or food availability. At a larger scale, latitude was the factor that encompassed these environmental gradients and explained the differences in LF of S. trutta. In summary, LF at age in stream-dwelling S. trutta decreases with latitude in Europe, the converse of Bergmann's rule. [source] | |||||||||||||||||||||||||||||||||||||||||||||||||