Applying statistical approaches in the dissection of genes versus environment for asthma and allergic disease

Now that some of the genes involved in asthma and allergy have been identified, interest is turning to how genetic predisposition interacts with exposure to environmental risk factors. These questions are best answered by studies in which both genotypes and other risk factors are measured, but even simpler studies, in which family history is used as a proxy for genotype, have made suggestive findings. For example, early breast feeding may increase the risk of allergic disease in genetically susceptible children, and decrease the risk of 'sporadic' allergy. This review also addresses the overall importance of genetic causes of allergic disease in the general population.

Solicitors disciplinary hearings in Queensland 1930-2000: a statistical analysis

This article reports on the results of a study undertaken by the author together with her research assistant, Heather Green. The study collected and analysed data from all disciplinary tribunal decisions heard in Queensland since 1930 in an attempt to provide empirical information which has previously been lacking. This article will outline the main features of the disciplinary system in Queensland, describe the research methodology used in the present study and then report on some findings from the study. Reported findings include a profile of solicitors who have appeared before a disciplinary hearing, the types of matters which have attracted formal discipline and the types of orders made by the tribunal. Much of the data is then presented on a time scale so as to reveal any changes over time.

Enhancing emancipatory systems methodologies for sustainable development

Our interest lies in applying the principles of critical systems thinking to human activity systems in developing countries in situations where issues of natural resource sustainability constrain the feasible set of long-term strategies. The concept of sustainable development provides an expanded domain for critical systems thinking. The fundamental values underpinning sustainable development are that both intragenerational and intergenerational equity are important. As a consequence, key stakeholders are often excluded from power-sharing within current social systems. Addressing these issues requires renewed focus on emancipatory commitment and methodologies. To date, Ulrich's critical systems heuristics is the only critical systems methodology that offers practicable tools for emancipation. A case study analysis in Tigray, northern Ethiopia, provides insights in relation to the application of critical system heuristics to issues of sustainable development and highlights the need to extend the use of critical systems heuristics beyond the design and monitoring of structured interventions.

The application of statistical process control charts to the detection and monitoring of hospital-acquired infections

The monitoring of infection control indicators including hospital-acquired infections is an established part of quality maintenance programmes in many health-care facilities. However, surveillance data use can be frustrated by the infrequent nature of many infections. Traditional methods of analysis often provide delayed identification of increasing infection occurrence, placing patients at preventable risk. The application of Shewhart, Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) statistical process control charts to the monitoring of indicator infections allows continuous real-time assessment. The Shewhart chart will detect large changes, while CUSUM and EWMA methods are more suited to recognition of small to moderate sustained change. When used together, Shewhart and EWMA methods are ideal for monitoring bacteraemia and multiresistant organism rates. Shewhart and CUSUM charts are suitable for surgical infection surveillance.

Bridging the gap between different statistical approaches: An integrated framework for modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.

Effect of changing data collection parameters on statistical motor unit number estimates

The effect of number of samples and selection of data for analysis on the calculation of surface motor unit potential (SMUP) size in the statistical method of motor unit number estimates (MUNE) was determined in 10 normal subjects and 10 with amyotrophic lateral sclerosis (ALS). We recorded 500 sequential compound muscle action potentials (CMAPs) at three different stable stimulus intensities (10–50% of maximal CMAP). Estimated mean SMUP sizes were calculated using Poisson statistical assumptions from the variance of 500 sequential CMAP obtained at each stimulus intensity. The results with the 500 data points were compared with smaller subsets from the same data set. The results using a range of 50–80% of the 500 data points were compared with the full 500. The effect of restricting analysis to data between 5–20% of the CMAP and to standard deviation limits was also assessed. No differences in mean SMUP size were found with stimulus intensity or use of different ranges of data. Consistency was improved with a greater sample number. Data within 5% of CMAP size gave both increased consistency and reduced mean SMUP size in many subjects, but excluded valid responses present at that stimulus intensity. These changes were more prominent in ALS patients in whom the presence of isolated SMUP responses was a striking difference from normal subjects. Noise, spurious data, and large SMUP limited the Poisson assumptions. When these factors are considered, consistent statistical MUNE can be calculated from a continuous sequence of data points. A 2 to 2.5 SD or 10% window are reasonable methods of limiting data for analysis. Muscle Nerve 27: 320–331, 2003

A comparison of the whole-body and segmental methodologies of bioimpedance analysis

Theory supports the use of a segmental methodology (SM) for bioimpedance analysis (BIA) of body water (BW). However, previous studies have generally failed to show a significant improvement when the SM is used in place of a whole-body methodology. A pilot study was conducted to compare the two methodologies in control and overweight subjects. BW of each subject was measured by D2O dilution and also estimated from BIA measurements. Bland and Altman analysis was used to compare the two values of BW. The SM resulted in a small but not significantly improved limits of agreement of measured and BIA estimated BW (psimilar to0.3). This and the results of previous studies suggest that improvements in prediction of BW obtained from application of the SM may be intrinsically small and may not justify the additional effort in application.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

Plano de implementa????o do projeto de desenvolvimento de capacidade de governan??a

Desde a d??cada de 1990, o Governo Federal brasileiro vem implementando uma agenda ambiciosa de reformas do Estado, centradas na redu????o da pobreza e na melhoria da efici??ncia dos servi??os p??blicos. As principais prioridades, conforme previstas no Plano Plurianual (PPA) para o per??odo 2003-2007, s??o as seguintes: inclus??o social e redu????o da desigualdade; crescimento econ??mico com gera????o de emprego; distribui????o de renda e respeito ao meio ambiente; promo????o e amplia????o dos direitos de cidadania; e fortalecimento da democracia. No in??cio de 2006, o Governo criou a Pol??tica Nacional de Desenvolvimento de Pessoal (Decreto 5.707), com o objetivo de melhorar e aumentar a efici??ncia e a efic??cia na presta????o de servi??os p??blicos. No marco dessa pol??tica recente, as escolas de administra????o p??blica desempenham um papel fundamental na identifica????o das compet??ncias que precisam ser desenvolvidas nas institui????es do governo, bem como na implementa????o de pol??ticas de capacita????o para os servidores p??blicos, diretamente e/ou em parceria com escolas de governo nos n??veis federal, estadual ou local. O Canad?? tamb??m est?? criando uma estrutura para levantar as compet??ncias necess??rias para os servidores p??blicos e desenvolv??-las como um componente da Renova????o do Servi??o P??blico em todo o governo. Como institui????es l??deres no desenvolvimento de compet??ncias de servidores p??blicos, a Canada School of Public Service (CSPS) e a Escola Nacional de Administra????o P??blica (ENAP) firmaram uma parceria para implementar o Projeto de Desenvolvimento de Capacidade de Governan??a no Brasil. A finalidade do Projeto ?? melhorar a capacidade de servidores p??blicos federais, estaduais e municipais do Brasil para desenvolver e implementar programas de capacita????o e gerenciar pol??ticas p??blicas descentralizadas. Espera-se que essa parceria e o resultante compartilhamento de experi??ncias em capacita????o para governan??a efetiva contribuam para a redu????o da pobreza e das desigualdades no Brasil, por meio do desenvolvimento de compet??ncias de servidores na presta????o de servi??os p??blicos eficazes e eficientes, voltados para o cidad??o. O Projeto re??ne, al??m das duas principais Escolas de Governo no Canad?? e no Brasil, seis Escolas Brasileiras de Administra????o P??blica regionais e duas renomadas Institui????es Acad??micas Canadenses ??? a Queen???s University e a Western Ontario University. O Minist??rio do Desenvolvimento Social e Combate ?? Fome (MDS) e tr??s Secretarias Especiais do Governo Federal ??? Ra??a (SEPPIR), Direitos Humanos (SEDH) e Pol??ticas para as Mulheres (SPM) ??? tamb??m se envolver??o nas atividades de compartilhamento de conhecimentos com o Human Resources and Skills Development Canada (HRSDC) e a Canada Public Service Agency (CPSA). A CIDA fornecer?? CND$1.700.000 por meio do Programa Brasil-Canad?? de Interc??mbio de Conhecimentos para a Promo????o da Equidade (PIPE). A contribui????o da ENAP ser?? de CND$1.069.707 em esp??cie. A CSPS contribuir?? com cursos, al??m de conhecimentos e suporte t??cnicos, avaliados em CND$1.000.000. Aproveitando a parceria entre a CSPS e a ENAP, que resultou na transfer??ncia e na adapta????o bem sucedidas de cursos e metodologias canadenses, o novo projeto extrapola o n??cleo do servi??o p??blico em Bras??lia, alcan??ando escolas de governo em regi??es brasileiras em situa????o de desvantagem. ?? semelhan??a do papel da CSPS no primeiro projeto, a ENAP fortalecer?? a capacidade das escolas parceiras regionais para capacitar servidores p??blicos envolvidos na presta????o de servi??os aos brasileiros. O interc??mbio estruturado entre Minist??rios dos Governos canadense e brasileiro tamb??m aplicar?? a aprendizagem mais diretamente a quest??es de pol??ticas e programas sociais do Brasil. O desafio assumido neste Projeto ?? a adapta????o de conhecimentos e aprendizagem, com vistas a melhorar a implementa????o de pol??ticas e programas sociais. Para tanto, a CSPS e a ENAP introduzir??o novos cursos nos curr??culos das escolas parceiras e incorporar??o novos m??todos e tecnologias de aprendizagem como, por exemplo, comunidades de pr??tica virtuais e um componente de tutoria (mentoring) envolvendo o Human Resources and Skills Development Canada e o Minist??rio do Desenvolvimento Social e Combate ?? Fome do Brasil. Seis institui????es da Rede Nacional de Escolas de Governo do Brasil e do Programa de Parceria da ENAP foram selecionadas e convidadas a se unir ?? CSPS e ?? ENAP nesse novo Projeto: a Universidade Federal do Par?? (UFPA), de Bel??m (estado do Par?? ??? regi??o Norte); a Funda????o Joaquim Nabuco (FUNDAJ), de Recife (Pernambuco ??? Nordeste); a Universidade Corporativa do Servi??o P??blico / Secretaria de Administra????o do Estado da Bahia (UCS/SAEB), Salvador (Bahia ??? Nordeste); a Escola de Governo do Mato Grosso do Sul (ESCOLAGOV), Campo Grande (estado do Mato Grosso do Sul ??? Centro-Oeste); a Escola Nacional de Ci??ncias Estat??sticas / Instituto Brasileiro de Geografia e Estat??stica (ENCE/IBGE), Rio de Janeiro (estado do Rio de Janeiro ??? Sudeste); e o Instituto Municipal de Administra????o P??blica (IMAP) de Curitiba (Paran?? ??? Sul). Essas escolas de refer??ncia foram escolhidas segundo sua capacidade de trabalhar como p??los de pr??ticas inovadoras em pol??ticas p??blicas e disseminar os benef??cios do Projeto para outras escolas em suas regi??es, por meio da Rede Nacional coordenada pela ENAP. O objetivo dessa parceria ?? fortalecer as escolas de governo locais, para que estas desenvolvam, por meio de eventos de aprendizagem, compet??ncias em servidores p??blicos, a fim de aumentar a capacidade do governo na implementa????o e gest??o de pol??ticas p??blicas. O Plano de Implementa????o do Projeto (PIP) descreve o trabalho a ser realizado por essas institui????es nos pr??ximos 30 meses, ao tempo em que serve de guia para os Parceiros do Projeto no que se refere ??s a????es e aos recursos necess??rios para a obten????o dos resultados acordados. Na medida em que o Projeto estiver em andamento e os parceiros iniciarem um interc??mbio produtivo de conhecimentos, o Plano de Trabalho Anual ser?? atualizado e revisto por meio de reuni??es anuais de avalia????o e encontros do Comit?? Diretor do Projeto, com vistas a assegurar que os resultados descritos no PIP sejam alcan??ados com sucesso

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.