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Horned grebe habitat use of constructed wetlands in Alberta, Canada

JOURNAL OF WILDLIFE MANAGEMENT 2012年第8期76卷 1694-1702页

作者： Kuczynski, Eva C. Paszkowski, Cynthia A. Gingras, Bev A. Univ Alberta Dept Biol Sci Edmonton AB T6G 2E9 Canada Environm Canada Edmonton AB T6B 2X3 Canada

Wetland degradation has resulted in declines in populations of aquatic birds throughout North America. Horned grebe (Podiceps auritus), a migratory diving bird experiencing population decline, may benefit from wetland construction. We examined horned grebe use of borrow pits (ponds created during highway construction) in Alberta, Canada. Our goals were to document patterns of occurrence and breeding success of grebes on borrow pits and to determine if occupied and unoccupied sites differed in local habitat or landscape characteristics. In May 2003 and 2007, 330 constructed ponds were surveyed for horned grebes. We chose 100 occupied and 100 unoccupied ponds for additional surveys in 2007 and 2008, and collected habitat and landscape data for these sites. We used generalized linear mixed model and generalized linear model regression, coupled with Akaike's Information Criterion, to determine which environmental variables were most effective in explaining occurrence of horned grebes. The best model included all measured local and landscape habitat features. Horned grebes occurred on 36% of ponds in May 2003 and 2007, and chicks were produced on 74.581.3% of occupied ponds in 2007 and 2008. Grebes occupied larger ponds with more emergent and riparian vegetation and avoided ponds that supported beavers, contained human structures, and were primarily surrounded by forest within 1?km. On ponds with grebe pairs, chicks were produced more often on sites surrounded by more riparian vegetation. We conclude that the construction of small wetlands offers a promising means of increasing breeding habitat for horned grebes and present specific recommendations for breeding pond construction. (c) 2012 The Wildlife Society.

关键词： borrow pit case-control constructed wetland generalized linear mixed model habitat selection horned grebe Peace Parkland Podiceps auritus waterbird

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Statistical analyses of chicken intestinal lesion scores in battery cage studies of anti-coccidial drugs

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VETERINARY PARASITOLOGY 2019年 272卷 83-94页

作者： Kang, Qing Vahl, Christopher, I Fan, Huihao Geurden, Thomas Ameis, Keith A. Taylor, Lucas P. Stat Intelligence Grp LLC Manhattan KS 66503 USA Kansas State Univ Dept Stat 101 Dickens Hall Manhattan KS 66506 USA Zoetis Vet Med Res & Dev Kalamazoo MI 49007 USA Zoetis Vet Med Res & Dev Mercuriusstr 20 B-1930 Zaventem Belgium

Establishing the efficacy of an anti-coccidial drug in poultry begins with conducting multiple battery cage studies, where the target animals are challenged with single and mixed Eimeria species inoculum under controlled laboratory conditions. One of the primary outcomes in a battery cage study is the intestinal lesion score defined on a discrete ordinal scale of 0 to 4. So far, the statistical analysis of lesion scores has routinely employed the linear mixed model (LMM). This present work proposes to apply the generalized linear mixed model (GLMM) with the cumulative logit link to statistically analyze coccidial lesion scores collected from battery cage studies. Upon applying this new approach on 9 datasets generated by challenging battery-cage-housed broilers with various mixtures of Eimeria species, it is observed that the GLMM fitted adequately to the data, produced variance component estimates that agreed with the experimental setup, and, at the 0.05 significance level, generated statistical results in complete concordance with the LMM approach. Advantages of the proposed GLMM over the LMM are discussed from several standpoints. Parallel to the regulatory requirement of a >= 1-unit reduction in the mean lesion score for clinical relevant efficacy under the LMM, the clinical relevancy criterion under the GLMM could be set as a >= 10-fold increase in the odds of having low lesion scores. That is, the effect of an anti-coccidial drug product would be deemed clinically relevant in battery-cage studies when the odds of having low lesion scores with the medication is 10 times or more than the odds without the medication.

关键词： Battery cage study Coccidiosis generalized linear mixed model Lesion score

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Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests

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STATISTICAL METHODS IN MEDICAL RESEARCH 2018年第8期27卷 2540-2553页

作者： Nikoloulopoulos, Aristidis K. Univ East Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

Copula mixed models for trivariate (or bivariate) meta-analysis of diagnostic test accuracy studies accounting (or not) for disease prevalence have been proposed in the biostatistics literature to synthesize information. However, many systematic reviews often include case-control and cohort studies, so one can either focus on the bivariate meta-analysis of the case-control studies or the trivariate meta-analysis of the cohort studies, as only the latter contains information on disease prevalence. In order to remedy this situation of wasting data we propose a hybrid copula mixed model via a combination of the bivariate and trivariate copula mixed model for the data from the case-control studies and cohort studies, respectively. Hence, this hybrid model can account for study design and also due to its generality can deal with dependence in the joint tails. We apply the proposed hybrid copula mixed model to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma.

关键词： generalized linear mixed model composite likelihood maximum likelihood sensitivity specificity prevalence

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A generalized mixed Logistic model for Predicting Individual Tree Survival Probability with Unequal Measurement Intervals

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FOREST SCIENCE 2013年第2期59卷 177-187页

作者： Yang, Yuqing Huang, Shongming Govt Alberta Dept Sustainable Resource Dev Div Forestry Edmonton AB Canada Govt Alberta Edmonton AB Canada

Based on a generalized linear mixed-model approach, three survival probability models were developed for lodgepole pine (Pinus contorta var. latifolia Engelm.) trees in boreal mixedwood stands in Alberta, Canada. Besides a traditional logistic model and a revised form of that model, a new logistic model was proposed to handle unequal measurement intervals typical of repeatedly measured growth-and-yield data. Various stand and tree characteristics were examined for their contribution to model improvement. Annual diameter increment, the ratio of basal area of larger trees to total stand basal area, and the basal area of larger deciduous trees were found to be significant predictors and were included in the final mixed models for predicting the probability of a tree surviving the coming growth period. Two random parameters were incorporated in the traditional and new models, one added to the intercept of the linear predictors and the other added to the parameter for annual diameter increment. One random parameter was added to the parameter for annual diameter increment in the revised traditional model. The new model provided improved stand density predictions at the population and plot levels over the two forms of the traditional model for a new data set. FOR. SCI. 59(2):177-187.

关键词： generalized linear mixed model unequal measurement interval individual tree mortality subject-specific prediction empirical best linear unbiased predictor

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Probabilistic Service Life model of Pavement Marking by Degradation Data

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TRANSPORTATION RESEARCH RECORD 2022年第10期2676卷 328-340页

作者： Mazzoni, Laura Nascimento Ho, Linda Lee Vasconcelos, Kamilla Lima Bernucci, Liedi L. B. Univ Sao Paulo Dept Transportat Engn Sao Paulo Brazil Univ Sao Paulo Dept Prod Engn Sao Paulo Brazil

Pavement markings are important features for improving road safety. As these markings must possess adequate retroreflectivity, road agencies are concerned about retroreflectivity maintenance. Consequently there is an increased focus on modeling the durability of pavement markings. Such models use time-to-failure data from life test experiments. The current study proposes a new method that consists of three steps: (i) designing an accelerated experiment for data collection;(ii) fitting a pavement marking retroreflectivity model using a generalized linear mixed model and the degradation data from the planned experiment, confirming the goodness of fit of the fitted model through the residual analysis;and (iii) conducting Monte Carlo simulation (with at least 10,000 runs of simulated vectors of the fitted model's coefficients) to obtain the pavement marking service life distribution for a failure retroreflectivity threshold level. The simulation of service life of pavement marking provides relevant information, such as service life expectation and empirical quantities of interest (as median). The proposed method is applied to data on retroreflectivity degradation obtained from an experimental test site, a Brazilian highway with a high traffic volume, with two fixed factors (waterborne paints and glass bead application rates, both with three levels). The model indicates that retroreflectivity decreases at an average rate of 2% per day, and how materials differ from each other. The proposed model is easily implementable and can help management teams to adequately plan the maintenance time for pavement markings.

关键词： waterborne paints generalized linear mixed model Monte Carlo simulation retroreflectivity reliability engineering

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Testing for Genetic Association in the Presence of Linkage and Gene-Covariate Interactions

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BIOMETRICAL JOURNAL 2010年第1期52卷 22-33页

作者： Callegaro, Andrea Lebrec, Jeremie J. P. Houwing-Duistermaat, Jeanine J. LUMC Dept Med Stat & Bioinformat NL-2300 RC Leiden Netherlands

In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus.

关键词： Family-based studies Family-based association tests generalized linear mixed model Linkage Score test

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Assessment of observer agreement for matched repeated binary measurements

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第5期56卷 1052-1060页

作者： Gao, Jingjing Pan, Yi Haber, Michael Emory Univ Ctr Comprehens Informat Atlanta GA 30322 USA Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA

Agreement is a broad term simultaneously covering evaluations of accuracy and precision of measurements. Assessment of observer agreement is based on the similarity between readings made on the same subject by different observers. The assessment of agreement on categorical observations is traditionally based on kappa or weighted kappa coefficients. However, kappa statistics have been criticized because they attain implausible values when the marginal distributions are skewed and/or unbalanced. New scaled indices called the coefficients of individual agreement (CIAs) have been developed for the assessment of individual observer agreement by comparing the observed disagreement between two observers to the disagreement between replicated observations made by the same observer on the same subject. This is based on the notion that under a good agreement, the disagreement between the two observers is usually not expected to exceed the disagreement between replicated observations of the same observer, and hence, a satisfactory agreement is established if these quantities are similar. This idea is extended and a new method utilizing the generalized linear mixed model is proposed to estimate the CIAs for binary data which consist of matched sets of repeated measurements made by the same observer under different conditions. The conditions may represent different time points, raters, laboratories, treatments, etc. The new approach allows the values of the measured variable and the magnitude of agreement to vary across the conditions. The reliability of the estimation method is examined via a simulation study. Data from a study aiming at determining the validity of diagnosis of breast cancer based on mammography are used to illustrate the new concepts and methods. (C) 2011 Elsevier By. All rights reserved.

关键词： Coefficient of individual agreement Observer agreement Binary data Repeated measurements generalized linear mixed model

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Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood

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BIOMETRICS 2024年第3期80卷 ujae092页

作者： Hu, Taojun Zhou, Yi Hattori, Satoshi Osaka Univ Grad Sch Med Dept Biomed Stat 2-2 Yamadaoka Suita Osaka 5650871 Japan Peking Univ Sch Publ Hlth Dept Biostat Beijing 100191 Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing 100871 Peoples R China Osaka Univ Inst Open & Transdisciplinary Res Initiat OTRI Integrated Frontier Res Med Sci Div Osaka 5650871 Japan

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.

关键词： generalized linear mixed model meta-analysis of sparse data publication bias research synthesis selection function sensitivity analysis

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Association between particulate matter concentration and symptoms of atopic dermatitis in children living in an industrial urban area of South Korea

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ENVIRONMENTAL RESEARCH 2018年 160卷 462-468页

作者： Oh, Inbo Lee, Jiho Ahn, Kangmo Kim, Jihyun Kim, Young-Min Sim, Chang Sun Kim, Yangho Univ Ulsan Coll Med Environm Hlth Ctr Ulsan South Korea Univ Ulsan Ulsan Univ Hosp Coll Med Dept Occupat & Environm Med Bangeojinsunhwan Ro 877 Ulsan 44033 South Korea Samsung Med Ctr Environm Hlth Ctr Atop Dis Seoul South Korea Sungkyunkwan Univ Sch Med Samsung Med Ctr Dept Pediat Seoul South Korea

Introduction: Increased exposure to particulate matter (PM) appears to increase the development of atopic diseases and allergic sensitization. This study evaluated the association between daily levels of PM with diameters less than 10 pm (PM10) and PM2.5 and symptoms of atopic dermatitis (AD) in children living in an industrial urban area. Methods: Indoor PM10 and PM2.5 concentrations were measured with an optical particle counter in two preschools near large industrial complexes in Ulsan, South Korea during two 6 -month periods (May October of 2012 and 2013). Twenty-one children with AD from these preschools were enrolled and observed daily for AD symptoms during the same periods. Indoor and outdoor PM concentrations were used to estimate PM exposure based on time activity patterns. Results: Analysis of PM10 and PM2.5 concentrations showed that indoor and outdoor PM10 levels varied similarly throughout each 6 -month period. In addition, indoor concentration of PM2.5 had high correlation with ambient outdoor concentration of PM10. Correlation analysis also indicated a significant positive correlation between the exacerbation of AD symptoms and daily mean exposure to PM10 and PM2.5. Based on a generalized linear mixed model (GLMM), PM exposure was significantly associated with the exacerbation of AD symptoms, with a maximum adjusted odds ratio (aOR) of 1.399 for a 10 mu g/m3 increase of PM2.5 (95% CI: 1.216-1.610). Discussion: Our findings suggest that short-term exposure to PM can exacerbate AD in young children living in an industrial urban area. PM2.5 had a stronger effect than PM10 on exacerbation of AD symptoms.

关键词： Particulate matter Atopic dermatitis Industrial urban areas Ulsan generalized linear mixed model

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Introduction to face recognition and evaluation of algorithm performance

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2013年 67卷 236-247页

作者： Givens, G. H. Beveridge, J. R. Phillips, P. J. Draper, B. Lui, Y. M. Bolme, D. Colorado State Univ Dept Stat Ft Collins CO 80523 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA NIST Gaithersburg MD 20899 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. A new opportunity for the application of statistical methods is driven by growing interest in biometric performance evaluation. Methods for performance evaluation seek to identify, compare and interpret how characteristics of subjects, the environment and images are associated with the performance of recognition algorithms. Some central topics in face recognition are reviewed for background and several examples of recognition algorithms are given. One approach to the evaluation problem is then illustrated with a generalized linear mixed model analysis of the Good, Bad, and Ugly Face Challenge, a pre-eminent face recognition dataset used to test state-of-the-art still-image face recognition algorithms. Findings include that (i) between-subject variation is the dominant source of verification heterogeneity when algorithm performance is good, and (ii) many covariate effects on verification performance are 'universal' across easy, medium and hard verification tasks. Although the design and evaluation of face recognition algorithms draw upon some familiar statistical ideas in multivariate statistics, dimension reduction, classification, clustering, binary response data, generalized linear models and random effects, the field also presents some unique features and challenges. Opportunities abound for innovative statistical work in this new field. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Face recognition Biometrics Computer vision generalized linear mixed model GLMM

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