43 resultados para Logistic regression model
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Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.
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The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.
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OBJECTIVE- To assess the relationship between clinical course after acute myocardial infarction (AMI) and diabetes treatment. RESEARCH DESIGN AND METHODS- Retrospective analysis of data from all patients aged 25-64 years admitted to hospitals in Perth, Australia, between 1985 and 1993 with AMI diagnosed according to the International Classification of Diseases (9th revision) criteria was conducted. Short- (28-day) and long-term survival and complications in diabetic and nondiabetic patients were compared. For diabetic patients, 28-day survival, dysrhythmias, heart block, and pulmonary edema were treated as outcomes, and factors related to each were assessed using multiple logistic regression. Diabetes treatment was added to the model to assess its significance. Long-term survival was compared by means of a Cox proportional hazards model. RESULTS- Of 5,715 patients, 745 (12.9%) were diabetic. Mortality at 28 days was 12.0 and 28.1% for nondiabetic and diabetic patients, respectively (P < 0.001); there were no significant drug effects in the diabetic group. Ventricular fibrillation in diabetic patients taking glibenclamide (11.8%) was similar to that of nondiabetic patients (11.0%) but was lower than that for those patients taking either gliclazide (18.0%; 0.1 > P > 0.05) or insulin (22.8%; P < 0.05). There were no other treatment-related differences in acute complications. Long-term survival in diabetic patients was reduced in those taking digitalis and/or diuretics but type of diabetes treatment at discharge had no significant association with outcome. CONCLUSlONS- These results do not suggest that ischemic heart disease should influence the choice of diabetes treatment regimen in general or of sulfonylurea drug in particular.
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Objective: To measure prevalence and model incidence of HIV infection. Setting: 2013 consecutive pregnant women attending public sector antenatal clinics in 1997 in Hlabisa health district, South Africa. Historical seroprevalence data, 1992-1995. Methods: Serum remaining from syphilis testing was tested anonymously for antibodies to HIV to determine seroprevalence. Two models, allowing for differential mortality between HIV-positive and HIV-negative people, were used. The first used serial seroprevalence data to estimate trends in annual incidence. The second, a maximum likelihood model, took account of changing force of infection and age-dependent risk of infection, to estimate age-specific HIV incidence in 1997. Multiple logistic regression provided adjusted odds ratios (OR) for risk factors for prevalent HIV infection. Results: Estimated annual HIV incidence increased from 4% in 1992/1993 to 10% in 1996/1997. In 1997, highest age-specific incidence was 16% among women aged between 20 and 24 years. in 1997, overall prevalence was 26% (95% confidence interval [CI], 24%-28%) and at 34% was highest among women aged between 20 and 24 years. Young age (<30 years; odds ratio [OR], 2.1; p = .001), unmarried status (OR 2.2; p = .001) and living in less remote parts of the district (OR 1.5; p = .002) were associated with HIV prevalence in univariate analysis. Associations were less strong in multivariate analysis. Partner's migration status was not associated with HIV infection. Substantial heterogeneity of HIV prevalence by clinic was observed (range 17%-31%; test for trend, p = .001). Conclusions: This community is experiencing an explosive HIV epidemic. Young, single women in the more developed parts of the district would form an appropriate cohort to test, and benefit from, interventions such as vaginal microbicides and HIV vaccines.
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No previous study has examined the modifying effect of menopausal status on the association between lactation and ovarian cancer risk. We recruited 824 epithelial ovarian cancer cases and 855 community controls in three Australian states, collecting reproductive and lactation histories by means of a contraceptive calendar and pregnancy and breastfeeding record. We report results in women with at least one liveborn infant for unsupplemented breastfeeding, in line with a biological model linking suppression of ovulation to reduction in ovarian cancer risk. We derived odds ratios from multiple logistic regression models including number of liveborn children, age, age at first or last birth, and other potential confounders, overall and by menopausal status. Estimates of relative risk of ovarian cancer per month of full lactation were 0.99 [95% confidence interval(CI) = 0.97-1.00] overall and 1.00 (95% CI = 0.99-1.01) and 0.98 (95% CI = 0.95-1.01) among post- and premenopausal women, respectively. We tailored a lactation variable to the incessant ovulation hypothesis by progressively discounting breastfeeding the longer after birth it occurred, finding odds ratios similar to those for the unmodified duration variable. We found no association of note among postmenopausal women. Breastfeeding seems to be somewhat protective against ovarian cancer, but only before menopause.
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Background and Aims: Hepatic steatosis has been shown to be associated with lipid peroxidation and hepatic fibrosis in a variety of liver diseases including non-alcoholic fatty liver disease. However, the lobular distribution of lipid peroxidation associated with hepatic steatosis, and the influence of hepatic iron stores on this are unknown. The aim of this study was to assess the distribution of lipid peroxidation in association with these factors, and the relationship of this to the fibrogenic cascade. Methods: Liver biopsies from 39 patients with varying degrees of hepatic steatosis were assessed for evidence of lipid peroxidation (malondialdehyde adducts), hepatic iron, inflammation, fibrosis, hepatic ;stellate cell activation (alpha-smooth muscle actin and TGF-beta expression) and collagen type I synthesis (procollagen a 1 (I) mRNA). Results: Lipid peroxidation occurred in and adjacent to fat-laden hepatocytes and was maximal in acinar zone 3. Fibrosis was associated with steatosis (P < 0.04), lipid peroxidation (P < 0.05) and hepatic iron stores (P < 0.02). Multivariate logistic regression analysis confirmed the association between steatosis and lipid peroxidation within zone 3 hepatocytes (P < 0.05), while for hepatic iron, lipid peroxidation was seen within sinusoidal cells (P < 0.05), particularly in zone 1 (P < 0.02). Steatosis was also associated with acinar inflammation (P < 0.005). α-Smooth muscle actin expression was present in association with both lipid peroxidation and fibrosis. Although the effects of steatosis and iron on lipid peroxidation and fibrosis were additive, there was no evidence of a specific synergistic interaction between them. Conclusions: These observations support a model where steatosis exerts an effect on fibrosis through lipid peroxidation, particularly in zone 3 hepatocytes. (C) 2001 Blackwell Science Asia Pty Ltd.
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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.
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Aim To assess the effectiveness of a program of computer-generated tailored advice for callers to a telephone helpline, and to assess whether it enhanced a series of callback telephone counselling sessions in aiding smoking cessation. Design Randomized controlled trial comparing: (1) untailored self-help materials; (2) computer-generated tailored advice only, and (3) computer-generated tailored advice plus callback telephone counselling. Assessment surveys were conducted at baseline, 3, 6 and 12 months. Setting Victoria, Australia. Participants A total of 1578 smokers who called the Quitline service and agreed to participate. Measurements Smoking status at follow-up; duration of cessation, if quit; use of nicotine replacement therapy; and extent of participation in the callback service. Findings At the 3-month follow-up, significantly more (chi(2)(2) = 16.9; P < 0.001) participants in the computer-generated tailored advice plus telephone counselling condition were not smoking (21%) than in either the computer-generated advice only (12%) or the control condition (12%). Proportions reporting not smoking at the 12-month follow-up were 26%, 23% and 22%, respectively (NS) for point prevalence, and for 9 months sustained abstinence; 8.2, 6.0, and 5.0 (NS). In the telephone counselling group, those receiving callbacks were more likely than those who did not to have sustained abstinence at 12 months (10.2 compared with 4.0, P < 0.05). Logistic regression on 3-month data showed significant independent effects on cessation of telephone counselling and use of NRT, but not of computer-generated tailored advice. Conclusion Computer-generated tailored advice did not enhance telephone counselling, nor have any independent effect on cessation. This may be due to poor timing of the computer-generated tailored advice and poor integration of the two modes of advice.
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Most of the modem developments with classification trees are aimed at improving their predictive capacity. This article considers a curiously neglected aspect of classification trees, namely the reliability of predictions that come from a given classification tree. In the sense that a node of a tree represents a point in the predictor space in the limit, the aim of this article is the development of localized assessment of the reliability of prediction rules. A classification tree may be used either to provide a probability forecast, where for each node the membership probabilities for each class constitutes the prediction, or a true classification where each new observation is predictively assigned to a unique class. Correspondingly, two types of reliability measure will be derived-namely, prediction reliability and classification reliability. We use bootstrapping methods as the main tool to construct these measures. We also provide a suite of graphical displays by which they may be easily appreciated. In addition to providing some estimate of the reliability of specific forecasts of each type, these measures can also be used to guide future data collection to improve the effectiveness of the tree model. The motivating example we give has a binary response, namely the presence or absence of a species of Eucalypt, Eucalyptus cloeziana, at a given sampling location in response to a suite of environmental covariates, (although the methods are not restricted to binary response data).
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Background: To investigate the association between selected social and behavioural (infant feeding and preventive dental practices) variables and the presence of early childhood caries in preschool children within the north Brisbane region. Methods: A cross sectional sample of 2515 children aged four to five years were examined in a preschool setting using prevalence (percentage with caries) and severity (dmft) indices. A self-administered questionnaire obtained information regarding selected social and behavioural variables. The data were modelled using multiple logistic regression analysis at the 5 per cent level of significance. Results: The final explanatory model for caries presence in four to five year old children included the variables breast feeding from three to six months of age (OR=0.7, CI=0.5, 1.0), sleeping with the bottle (OR=1.9, CI=1.5, 2.4), sipping from the bottle (OR=1.6, CI=1.2, 2.0), ethnicity other than Caucasian (OR=1.9, CI=1.4, 2.5), annual family income $20,000-$35,000 (OR = 1.7, CI=1.3, 2.3) and annual family income less than $20,000 (OR=2.1, CI=1.5, 2.8). Conclusion: A statistical model for early childhood caries in preschool children within the north Brisbane region has been constructed using selected social and behavioural determinants. Epidemiological data can be used for improved public oral health service planning and resource allocation within the region.
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Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate. spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that: approximately 27000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network. (C) 2004 Elsevier Ltd. All tights reserved.
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Objective: To investigate gender-specific relationships between self-reported sexual abuse, antisocial behaviour and substance use in a large community sample of adolescents. Method: A cross-sectional study of students aged, on average, 13 (n = 2596), 14 (n = 2475) and 15 years (n = 2290), from 27 schools in South Australia with a questionnaire including sexual abuse, frequency and severity of substance use, depressive symptomatology (CES-D), family functioning (McMaster Family Assessment Device), and antisocial behaviour (an adapted 22-item Self-Report Delinquency Scale). Logistic regression analyses using HLM V5.05 with a population-average model were conducted. Results: In the model considered, reported sexual abuse is significantly independently associated with antisocial behaviour, controlling for confounding factors of depressive symptomatology and family dysfunction, with increased risks of three- to eightfold for sexually abused boys, and two- to threefold for sexually abused girls, compared to nonabused. Increased risks of extreme substance use in sexually abused girls (age 13) and boys (ages 13-15) are more than fourfold, compared to nonabused. Age differences were not statistically significant. Conclusion: Childhood sexual abuse is a risk factor for the development of antisocial behaviour and substance use in young adolescents. Clinicians should be aware of gender differences.
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The present paper develops and tests a model explaining public sector derivative use in terms of budget discrepancy minimization. The model is different from private sector models. Private sector models do not readily translate into the public sector, which typically faces different objectives. Hypotheses are developed and tested using logistic regression over a sample of Australian Commonwealth public sector organizations. It is found that public sector organization derivative use is positively correlated with liabilities and size consistent with the hypotheses concerning budget discrepancy management.
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Associations between parenting style and depressive symptomatology in a community sample of young adolescents (N = 2596) were investigated using self-report measures including the Parental Bonding Instrument and the Center for Epidemiologic Studies Depression Scale. Specifically, the 25-item 2-factor and 3-factor models by Parker et al. (1979), Kendler's (1996) 16-item 3-factor model, and Parker's (1983) quadrant model for the Parental Bonding Instrument were compared. Data analysis included analysis of variance and logistic regression. Reanalysis of Parker's original scale indicates that overprotection is composed of separate factors: intrusiveness (at the individual level) and restrictiveness (in the social context). All models reveal significant independent contributions from paternal care, maternal care, and maternal overprotection (2-factor) or intrusiveness (3-factor) to moderate and serious depressive symptomatology, controlling for sex and family living arrangement. Additive rather than multiplicative interactions between care and overprotection were found. Regardless of the level of parental care and affection, clinicians should note that maternal intrusiveness is strongly associated with adverse psychosocial health in young adolescents.
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Demonstrating the existence of trends in monitoring data is of increasing practical importance to conservation managers wishing to preserve threatened species or reduce the impact of pest species. However, the ability to do so can be compromised if the species in question has low detectability and the true occupancy level or abundance of the species is thus obscured. Zero-inflated models that explicitly model detectability improve the ability to make sound ecological inference in such situations. In this paper we apply an occupancy model including detectability to data from the initial stages of a fox-monitoring program on the Eyre Peninsula, South Australia. We find that detectability is extremely low (< 18%) and varies according to season and the presence or absence of roadside vegetation. We show that simple methods of using monitoring data to inform management, such as plotting the raw data or performing logistic regression, fail to accurately diagnose either the status of the fox population or its trajectory over time. We use the results of the detectability model to consider how future monitoring could be redesigned to achieve efficiency gains. A wide range of monitoring programs could benefit from similar analyses, as part of an active adaptive approach to improving monitoring and management.