bluuebunny/crossref_metadata_2025_split huggingface dataset:
A retrospective study of climatic suitability for the tick Rhipicephalus (Boophilus)microplus in the Americas
Authors: Estrada‐Peña A. , Acedo C. Sánchez , Quílez J. , Del Cacho E. 
Published: September 2005

ABSTRACTAim  Climate change has the potential to have an impact on the distribution of ticks and tick‐borne diseases. This paper identifies the changes in climate suitability for the tick Rhipicephalus (Boophilus) microplus in the Americas by analysing climate data for the period 1950–99.Location  The model was applied to the American continent.Methods  A model based on Environmental Niche Modelling was used on a gridded (0.5°) long‐term (1950–99) climate data set. A map of the core range of the species was constructed, and areas where habitat suitability (HS) changes suddenly over short time periods were identified as regions of high sensitivity. Tendency of climate in the continent was evaluated and scenarios constructed for 2025 and 2050.Results  Regions of high sensitivity included the southern USA, Mexico and western and central Argentina. Analysis of climate variables in these regions identified water vapour pressure deficit and evaporation as underlying the high sensitivity of habitat suitability in the USA and Mexico, and showed that episodes of high variability are linked to the El Niño Southern Oscillation. Projections of the tendency of HS as observed for the 1950–99 period point to an increase in this value in parts of the southern USA and in central Argentina, a finding that can be attributed to the progressive increase in minimum and yearly averaged temperatures.Conclusions  Short‐term changes in climate may drive the system into unstable situations with sudden changes in habitat suitability for the target tick in specific zones of the Americas. Results suggest an increased abiotic (climate) suitability for R. microplus in areas whose habitat is currently unsuitable for this species.
Plant allometry, stoichiometry and the temperature‐dependence of primary productivity
Authors: Kerkhoff Andrew J. , Enquist Brian J. , Elser James J. , Fagan William F. 
Published: September 2005

ABSTRACTAim While physical constraints influence terrestrial primary productivity, the extent to which geographical variation in productivity is influenced by physiological adaptations and changes in vegetation structure is unclear. Further, quantifying the effect of variability in species traits on ecosystems remains a critical research challenge. Here, we take a macroecological approach and ask if variation in the stoichiometric traits (C: N: P ratios) of plants and primary productivity across global‐scale temperature gradients is consistent with a scaling model that integrates recent insights from the theories of metabolic scaling and ecological stoichiometry.Location This study is global in scope, encompassing a wide variety of terrestrial plant communities.Methods We first develop a scaling model that incorporates potentially adaptive variation in leaf and whole‐plant nutrient content, kinetic aspects of photosynthesis and plant respiration, and the allometry of biomass partitioning and allocation. We then examine extensive data sets concerning the stoichiometry and productivity of diverse plant communities in light of the model.Results Across diverse ecosystems, both foliar stoichiometry (N : P) and ‘nitrogen productivity’ (which depends on both community size structure and plant nutrient content) vary systematically across global scale temperature gradients. Primary productivity shows no relationship to temperature.Main conclusions The model predicts that the observed patterns of variation in plant stoichiometry and nutrient productivity may offset the temperature dependence of primary production expected from the kinetics of photosynthesis alone. Our approach provides a quantitative framework for treating potentially adaptive functional variation across communities as a continuum and may thus inform studies of global change. More generally, our approach represents one of the first explicit combinations of ecological stoichiometry and metabolic scaling theories in the analysis of macroecological patterns.
Survey Response and Survey Characteristics: Microlevel Evidence from the European Community Household Panel
Authors: Nicoletti Cheti , Peracchi Franco 
Published: September 2005

SummaryThe paper presents microlevel evidence on the role of the sociodemographic characteristics of a population and the characteristics of the data collection process as predictors of survey response. Our evidence is based on the public use files of the European Community Household Panel, a longitudinal household survey covering the countries of the European Union, whose attractive feature is the high level of comparability across countries and over time. We model the response process as the outcome of two sequential events: contact between the interviewer and an eligible interviewee, and co-operation by the interviewee. Our model allows for dependence between the ease of contact and the propensity to co-operate, taking into account the censoring problem caused by the fact that we observe whether a person is a respondent only if she has been contacted.
A Review of Cancer Screening Evaluation Techniques, with some Particular Examples in Breast Cancer Screening
Authors: Warwick Jane , Duffy Stephen W. 
Published: September 2005

SummaryThe instigation of mass screening for breast cancer has, over the last three decades, raised various statistical issues and led to the development of new statistical approaches. Initially, the design of screening trials was the main focus of research but, as the evidence in favour of population-based screening programmes mounts, a variety of other applications have also been identified. These include administrative and quality control tasks, for monitoring routine screening services, as well as epidemiological modelling of incidence and mortality. We review the commonly used methods of cancer screening evaluation, highlight some current issues in breast screening and, using examples from randomized trials and established screening programmes, illustrate the role that statistical science has played in the development of clinical research in this field.
Resolving Paradoxes Involving Surrogate End Points
Authors: Baker Stuart G. , Izmirlian Grant , Kipnis Victor 
Published: September 2005

SummaryWe define a surrogate end point as a measure or indicator of a biological process that is obtained sooner, at less cost or less invasively than a true end point of health outcome and is used to make conclusions about the effect of an intervention on the true end point. Prentice presented criteria for valid hypothesis testing of a surrogate end point that replaces a true end point. For using the surrogate end point to estimate the predicted effect of intervention on the true end point, Day and Duffy assumed the Prentice criterion and arrived at two paradoxical results: the estimated predicted intervention effect by using a surrogate can give more precise estimates than the usual estimate of the intervention effect by using the true end point and the variance is greatest when the surrogate end point perfectly predicts the true end point. Begg and Leung formulated similar paradoxes and concluded that they indicate a flawed conceptual strategy arising from the Prentice criterion. We resolve the paradoxes as follows. Day and Duffy compared a surrogate-based estimate of the effect of intervention on the true end point with an estimate of the effect of intervention on the true end point that uses the true end point. Their paradox arose because the former estimate assumes the Prentice criterion whereas the latter does not. If both or neither of these estimates assume the Prentice criterion, there is no paradox. The paradoxes of Begg and Leung, although similar to those of Day and Duffy, arise from ignoring the variability of the parameter estimates irrespective of the Prentice criterion and disappear when the variability is included. Our resolution of the paradoxes provides a firm foundation for future meta-analytic extensions of the approach of Day and Duffy.
A Protective Estimator for Longitudinal Binary Data Subject to Non-Ignorable Non-Monotone Missingness
Authors: Fitzmaurice Garrett M. , Lipsitz Stuart R. , Molenberghs Geert , Ibrahim Joseph G. 
Published: September 2005

SummaryIn longitudinal studies missing data are the rule not the exception. We consider the analysis of longitudinal binary data with non-monotone missingness that is thought to be non-ignorable. In this setting a full likelihood approach is complicated algebraically and can be computationally prohibitive when there are many measurement occasions. We propose a ‘protective’ estimator that assumes that the probability that a response is missing at any occasion depends, in a completely unspecified way, on the value of that variable alone. Relying on this ‘protectiveness’ assumption, we describe a pseudolikelihood estimator of the regression parameters under non-ignorable missingness, without having to model the missing data mechanism directly. The method proposed is applied to CD4 cell count data from two longitudinal clinical trials of patients infected with the human immunodeficiency virus.
The Professionalization of the ‘Shoe Clerk’
Authors: Grieve Andrew P. 
Published: September 2005

SummaryThe address reviews the professionalization of statisticians working in the regulated world of drug development. The need for statisticians to continue to push the boundaries of their professional environment is stressed. It is argued that to maintain the strength and influence of the profession it is neither desirable, nor necessary, to define in a statutory sense access to the profession.
Estimating Utilities from Individual Health Preference Data: A Nonparametric Bayesian Method
Authors: Kharroubi Samer A. , O’Hagan Anthony , Brazier John E. 
Published: September 2005

SummaryA fundamental benefit that is conferred by medical treatments is to increase the health-related quality of life (HRQOL) that is experienced by patients. Various descriptive systems exist to define a patient’s health state, and we address the problem of assigning an HRQOL value to any given state in such a descriptive system. Data derive from experiments in which individuals are asked to assign their personal values to various health states. We construct a Bayesian model that takes account of various important aspects of such data. Specifically, we allow for the repeated measures feature that each individual values several different states, and the fact that individuals vary markedly in their valuations, with some people consistently providing higher valuations than others. We model the relationship between HRQOL and health state nonparametrically. We illustrate our method by using data from an experiment in which 611 individuals each valued up to six states in the descriptive system known as the SF-6D system. Although the SF-6D system distinguishes 18000 different health states, only 249 of these were valued in this experiment. We provide posterior inference about the HRQOL values for all 18000 states.
Correlating Two Continuous Variables Subject to Detection Limits in the Context of Mixture Distributions
Authors: Chu Haitao , Moulton Lawrence H. , Mack Wendy J. , Passaro Douglas J. , Barroso Paulo F. , Muñoz Alvaro 
Published: September 2005

Summary
In individuals who are infected with human immunodeficiency virus (HIV), distributions of quantitative HIV ribonucleic acid measurements may be highly left censored with an extra spike below the limit of detection LD of the assay. A two-component mixture model with the lower component entirely supported on [0, LD] is recommended to model the extra spike in univariate analysis better. Let LD1 and LD2 be the limits of detection for the two HIV viral load measurements. When estimating the correlation coefficient between two different measures of viral load obtained from each of a sample of patients, a bivariate Gaussian mixture model is recommended to model the extra spike on [0, LD1] and [0, LD2] better when the proportion below LD is incompatible with the left-hand tail of a bivariate Gaussian distribution. When the proportion of both variables falling below LD is very large, the parameters of the lower component may not be estimable since almost all observations from the lower component are falling below LD. A partial solution is to assume that the lower component’s entire support is on [0, LD1]×[0, LD2]. Maximum likelihood is used to estimate the parameters of the lower and higher components. To evaluate whether there is a lower component, we apply a Monte Carlo approach to assess the p-value of the likelihood ratio test and two information criteria: a bootstrap-based information criterion and a cross-validation-based information criterion. We provide simulation results to evaluate the performance and compare it with two ad hoc estimators and a single-component bivariate Gaussian likelihood estimator. These methods are applied to the data from a cohort study of HIV-infected men in Rio de Janeiro, Brazil, and the data from the Women’s Interagency HIV oral study. These results emphasize the need for caution when estimating correlation coefficients from data with a large proportion of non-detectable values when the proportion below LD is incompatible with the left-hand tail of a bivariate Gaussian distribution.
Predicting Future Discoveries of European Marine Species by Using a Non-Homogeneous Renewal Process
Authors: Wilson Simon P. , Costello Mark J. 
Published: September 2005

SummaryPredicting future rates of species discovery and the number of species remaining are important in efforts to preserve biodiversity, discussions on the rate of species extinction and comparisons on the state of knowledge of animals and plants of different taxa. Data on discovery dates of species in 32 European marine taxa are analysed by using a class of thinned temporal renewal process models. These models allow for both underdispersion and overdispersion with respect to the non-homogeneous Poisson process. An approach for implementing Bayesian inference for these models is described that uses Markov chain Monte Carlo simulation and that is applicable to other types of thinned process. Predictions are made on the number of species remaining to be discovered in each taxon.
Bayesian Model Selection for Join Point Regression with Application to Age-Adjusted Cancer Rates
Authors: Tiwari Ram C. , Cronin Kathleen A. , Davis William , Feuer Eric J. , Yu Binbing , Chib Siddhartha 
Published: September 2005

SummaryThe method of Bayesian model selection for join point regression models is developed. Given a set of K+1 join point models M0, M1, …, MK with 0, 1, …, K join points respec-tively, the posterior distributions of the parameters and competing models Mk are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model Mk with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model Mk with the largest posterior probability as the best model when the prior distribution of Mk is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance σ2, and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute’s join point software (http://www.srab.cancer.gov/joinpoint/) and will be available to the public.
Near-Optimal Designs for Dual Channel Microarray Studies
Authors: Wit Ernst , Nobile Agostino , Khanin Raya 
Published: September 2005

SummaryMuch biological and medical research employs microarray studies to monitor gene expression levels across a wide range of organisms and under many experimental conditions. Dual channel microarrays are a common platform and allow two samples to be measured simultaneously. A frequently used design uses a common reference sample to make conditions across different arrays comparable. Our aim is to formulate microarray experiments in the experimental design context and to use simulated annealing to search for near-optimal designs. We identify a subclass of designs, the so-called interwoven loop designs, that seems to have good optimality properties compared with the near-optimal designs that are found by simulated annealing. Commonly used reference designs and dye swap designs are shown to be highly inefficient.
The Neglog Transformation and Quantile Regression for the Analysis of a Large Credit Scoring Database
Authors: Whittaker Joe , Whitehead Chris , Somers Mark 
Published: September 2005

SummaryA statistical analysis of a bank’s credit card database is presented. The database is a snapshot of accounts whose holders have missed a payment on a given month but who do not subsequently default. The variables on which there is information are observable measures on the account (such as profit and activity), and whether actions that are available to the bank (such as letters and telephone calls) have been taken. A primary objective for the bank is to gain insight into the effect that collections activity has on on-going account usage. A neglog transformation that highlights features that are hidden on the original scale and improves the joint distribution of the covariates is introduced. Quantile regression, a novel methodology to the credit scoring industry, is used as it is relatively assumption free, and it is suspected that different relationships may be manifest in different parts of the response distribution. The large size is handled by selecting relatively small subsamples for training and then building empirical distributions from repeated samples for validation. In the application to the database of clients who have missed a single payment a substantive finding is that the predictor of the median of the target variable contains different variables from those of the predictor of the 30% quantile. This suggests that different mechanisms may be at play in different parts of the distribution.
Analysis of Familial Aggregation in the Presence of Varying Family Sizes
Authors: Matthews Abigail G. , Finkelstein Dianne M. , Betensky Rebecca A. 
Published: September 2005

SummaryFamily studies are frequently undertaken as the first step in the search for genetic and/or environmental determinants of disease. Significant familial aggregation of disease is suggestive of a genetic aetiology for the disease and may lead to more focused genetic analysis. Of course, it may also be due to shared environmental factors. Many methods have been proposed in the literature for the analysis of family studies. One model that is appealing for the simplicity of its computation and the conditional interpretation of its parameters is the quadratic exponential model. However, a limiting factor in its application is that it is not reproducible, meaning that all families must be of the same size. To increase the applicability of this model, we propose a hybrid approach in which analysis is based on the assumption of the quadratic exponential model for a selected family size and combines a missing data approach for smaller families with a marginalization approach for larger families. We apply our approach to a family study of colorectal cancer that was sponsored by the Cancer Genetics Network of the National Institutes of Health. We investigate the properties of our approach in simulation studies. Our approach applies more generally to clustered binary data.
Perceived preservation format and food preference
Authors: Hurling Robert , Martin Karen J. 
Published: September 2005

AbstractThe same (chilled) food product was presented to 77 consumers with either a frozen or chilled label. As predicted, liking for the food product was found to be in line with consumer expectation of liking for the different preservation format labels. For example, consumers that expected chilled foods to taste better than frozen reported higher liking for the product with the chilled label, even though actual quality of all products tasted was the same. Contrary to our prediction that tasting similar quality products perceived to be from different preservation formats would bring expectation of liking for those formats closer together, we found consumers who expressed no preference before tasting the products had developed a preference afterwards. We discuss the implication of these results and similarities between the concepts of conditioning, attitude, expectation and preference.
Consumers’ perception of food packaging in Trinidad, West Indies and its related impact on food choices
Authors: Peters‐Texeira Angela , Badrie Neela 
Published: September 2005

AbstractThe purpose of the study was to investigate consumers’ perception of food packaging and its impact on food choices. The study population comprised 82 people who were ultimately consumers of packaged food products. The sample was drawn from six major supermarkets located in different geographical areas in Trinidad, West Indies. Data collection was carried out by using a questionnaire based on five topics: visual impact or attractiveness of the packaging; type of packaging material; labelling and nutritional information; new products; and fruit preserves. The packaging feature that influenced most of the respondents’ choice of products was information on the label (41.5%); it was followed by quality and type of packaging (24.4%), brand name/popularity (22.0%) and visual impact (12.2%). When asked if they would purchase a product that was most attractively packaged, 85.4% responded in the affirmative. Most respondents (92.7%) believed that packaging material could adversely affect the quality of performance of a food product. Also, 92.7% of respondents agreed that nutrition information should be shown on all food products, although 36.6% admitted that they do not read the label because of its complexity. Influence of gender was not significant (P < 0.05) on consumer perception of food packaging and on food choices. Although the sample was small, the data highlighted the need to educate consumers of packaged foods, so that informed decisions could be taken in respect to food quality, safety and nutrition.