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Selected AbstractsThe Epidemiology of Convulsive Status Epilepticus in Children: A Critical ReviewEPILEPSIA, Issue 9 2007Miquel Raspall-Chaure Summary:, There is ongoing debate regarding the most appropriate definition of status epilepticus. This depends upon the research question being asked. Based on the most widely used "30 min definition," the incidence of childhood convulsive status epilepticus (CSE) in developed countries is approximately 20/100,000/year, but will vary depending, among others, on socioeconomic and ethnic characteristics of the population. Age is a main determinant of the epidemiology of CSE and, even within the pediatric population there are substantial differences between older and younger children in terms of incidence, etiology, and frequency of prior neurological abnormalities or prior seizures. Overall, incidence is highest during the first year of life, febrile CSE is the single most common cause, around 40% of children will have previous neurological abnormalities and less than 15% will have a prior history of epilepsy. Outcome is mainly a function of etiology. However, the causative role of CSE itself on mesial temporal sclerosis and subsequent epilepsy or the influence of age, duration, or treatment on outcome of CSE remains largely unknown. Future studies should aim at clarifying these issues and identifying specific ethnic, genetic, or socioeconomic factors associated with CSE to pinpoint potential targets for its primary and secondary prevention. [source] In situ-based effects measures: Determining the ecological relevance of measured responsesINTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, Issue 2 2007Donald J Baird Abstract The aim of this review is to examine how the choice of test species and study design employed in the use of in situ approaches in ecological risk assessment can maximize the ecological relevance of data. We provide a framework to define and assess ecological relevance that permits study designs to remain focused on the ecological question being addressed. This framework makes explicit the linkages between effects at lower levels of biological organization and higher-order ecological effects at the population, community, and ecosystem levels. The usefulness of this framework is illustrated by reference to specific examples from aquatic ecotoxicology. The use of models as both interpretive and predictive tools is discussed, with suggestions of appropriate methods for different protection goals. [source] Are parametric models suitable for estimating avian growth rates?JOURNAL OF AVIAN BIOLOGY, Issue 4 2007William P. Brown For many bird species, growth is negative or equivocal during development. Traditional, parametric growth curves assume growth follows a sigmoidal form with prescribed inflection points and is positive until asymptotic size. Accordingly, these curves will not accurately capture the variable, sometimes considerable, fluctuations in avian growth over the course of the trajectory. We evaluated the fit of three traditional growth curves (logistic, Gompertz, and von Bertalanffy) and a nonparametric spline estimator to simulated growth data of six different specified forms over a range of sample sizes. For all sample sizes, the spline best fit the simulated model that exhibited negative growth during a portion of the trajectory. The Gompertz curve was the most flexible for fitting simulated models that were strictly sigmoidal in form, yet the fit of the spline was comparable to that of the Gompertz curve as sample size increased. Importantly, confidence intervals for all of the fitted, traditional growth curves were wholly inaccurate, negating the apparent robustness of the Gompertz curve, while confidence intervals of the spline were acceptable. We further evaluated the fit of traditional growth curves and the spline to a large data set of wood thrush Hylocichla mustelina mass and wing chord observations. The spline fit the wood thrush data better than the traditional growth curves, produced estimates that did not differ from known observations, and described negative growth rates at relevant life history stages that were not detected by the growth curves. The common rationale for using parametric growth curves, which compress growth information into a few parameters, is to predict an expected size or growth rate at some age or to compare estimated growth with other published estimates. The suitability of these traditional growth curves may be compromised by several factors, however, including variability in the true growth trajectory. Nonparametric methods, such as the spline, provide a precise description of empirical growth yet do not produce such parameter estimates. Selection of a growth descriptor is best determined by the question being asked but may be constrained by inherent patterns in the growth data. [source] Therapist effects in randomised controlled trials: what to do about themJOURNAL OF CLINICAL NURSING, Issue 7-8 2010Stephen J Walters Aims and objectives., The aim of this study is to describe and compare three statistical methods to allow for therapist effects in individually randomised controlled trials. Background., In an individually randomised controlled trial where the intervention is delivered by a health professional it seems likely that the effectiveness of the intervention, independent of any treatment effect, could depend on the skill of the health professional delivering it. This leads to a potential clustering of the outcomes for the patients being treated by the same health professional. Design., Retrospective statistical analysis of outcomes from four example randomised controlled trial datasets with potential clustering by health professional. Methods., Three methods to allow for clustering are described: cluster level analysis; random effects models and marginal models. These models were fitted to continuous outcome data from four example randomised controlled trial datasets with potential clustering by health professional. Results., The cluster level models produced the widest confidence intervals. Little difference was found between the estimates of the regression coefficients for the treatment effect and confidence intervals between the individual patient level models for the datasets. The conclusions reached for each dataset match those published in the original papers. The intracluster correlation coefficient ranged from <0ˇ001,0ˇ04 for the outcomes, which shows only minor levels of clustering within the datasets. Conclusions., The models, which use individual level data are to be preferred. Treatment coefficients from these models have different interpretations. The choice of model should depend on the scientific question being asked. Relevance to clinical practice., We recommend that researchers should be aware of any potential clustering, by health professional, in their randomised controlled trial and use appropriate methods to account for this clustering in the statistical analysis of the data. [source] Interpreting and estimating measures of community phylogenetic structuringJOURNAL OF ECOLOGY, Issue 5 2008Olivier J. Hardy Summary 1To characterize the spatial phylogenetic structure of communities, Hardy & Senterre (2007) (J. Ecol., 95, 493,506) partition Gini,Simpson diversity and its generalization, Rao's entropy, defining IST and PST as the proportion of diversity expressed among sites. 2Interpreting IST as a measure of ,differentiation' between sites is inadequate because low values are actually compatible with high differentiation (low species sharing) in species rich communities. To avoid an inadequate use of IST, for example in conservation biology, we offer a more literal interpretation: IST expresses the ,local species identity excess'. Similarly, PST expresses the ,local phylogenetic similarity excess'. 3Villéger & Mouillot (2008) (J. Ecol., 96, 845,848, this issue) argue that the equations of Hardy & Senterre (2007) to compute diversity are inadequate when sites differ in size, and they provide new expressions weighting sites by their sizes. We argue that whether sites must be weighted equally or not depends on the question being asked. Moreover, actual size and sample size must be distinguished, the latter being important for defining estimators. 4Synthesis. The formulations given by Hardy & Senterre (2007) and by Villéger & Mouillot (2008) are both correct in the specific contexts we detail. [source] Updating an input,output table for use in policy analysisAUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 4 2000Benjamin L. Buetre The long lag in the publication of input,output tables is one of the central constraints in applied general equilibrium analysis. Model builders often use out-dated databases leading to analyses that are inappropriate for the policy questions being addressed. This occurs particularly when there exists a significant structural change in the economy. We discuss the updating of an input,output table of the Philippines by simulation technique. A detailed computable general equilibrium model of the Philippine economy with comparative static and forecasting capabilities is utilised. The data are drawn from known percentage changes of macroeconomic variables such as those in the national accounts and structural variables such as employment and output by industry. [source] | |||||||||||||