Search results for "Logistic Model"

Model building strategies for risk analysis of perioperative histamine-related cardiorespiratory disturbances.

1995

The Impact of the Direct Participation of Workers on the Rates of Absenteeism in the Spanish Labor Environment.

2020

The aim of this research was to study the relationship between the different levels of direct participation of workers (passive, consultative or active-delegated) in risk prevention management with the levels of absenteeism in Spain. To this end, a transversal study was carried out using microdata from the Second European Survey of Companies on New and Emerging Risks (ESENER-2-Spain, 2014) with a master population of 3162 work centres. A multinomial logistic regression model was carried out, with the dependent variable being the levels of absenteeism and the independent variables, the participation indicators and preventive management, calculating the adjusted odds ratio (aOR) between all t…

Alpha1-antitrypsin heterozygosity plays a positive role in attainment of longevity.

2007

Genes involved in cardiovascular diseases (CVD) play an opposite role in human longevity. The alpha1-antitrypsin (AAT) is a serine-protease inhibitor required for the prevention of proteolytic tissue damage, by neutrophil elastase. The role of AAT in CVD has not been definitively assessed and its effect on longevity has not yet fully been studied. To clarify these points, we have studied the distribution of AAT allele variants in 3 cohorts: 127 young patients affected by acute myocardial infarction (AMI), 255 young controls and 143 centenarians from Sicily. The Z allele frequency was most frequent in centenarians (13.3%), intermediate in healthy young controls (3.1%) and less frequent in AM…

Les élèves en accompagnement scolaire : adéquation entre public visé et public accueilli

2002

This article examines the adjustment between pupils targeted to study in after school programs and pupils who are already in attendance. Several analyses have been drawn up on a large cross-section of seven and ten year-old pupils and have shown that most of them have social problems and/or are under-achieving. However, a third of the cross-section are not experiencing difficulties and therefore should not have been enrolled. Regarding the selection process of the after school programs, the analysis also shows that weak and foreign pupils more frequently seek school support after the normal school day.

Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

2010

The use of bacteriophages provides an attractive approach to the fight against food-borne pathogenic bacteria, since they can be found in different environments and are unable to infect humans, both characteristics of which support their use as biocontrol agents. Two lytic bacteriophages, vB_SauS-phiIPLA35 (phiIPLA35) and vB_SauS-phiIPLA88 (phiIPLA88), previously isolated from the dairy environment inhibited the growth of Staphylococcus aureus. To facilitate the successful application of both bacteriophages as biocontrol agents, probabilistic models for predicting S. aureus inactivation by the phages in pasteurized milk were developed. A linear logistic regression procedure was used to desc…

A penalized approach for the bivariate ordered logistic model with applications to social and medical data

2018

Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios to be estimated also increases, and estimation gets problematic. In this work we propose a non-parametric approach for the maximum likelihood (ML) estimation of a BOLM, wherein penalties to the differences between adjacent row and column effects are applied. Our proposal is then compared to the Goodman and Dale models. Some simulation results as well as analyses of two real data sets are presented and discussed.

Cluster-Localized Sparse Logistic Regression for SNP Data

2012

The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous…

2012

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedica…

Inferential tools in penalized logistic regression for small and sparse data: A comparative study.

2016

This paper focuses on inferential tools in the logistic regression model fitted by the Firth penalized likelihood. In this context, the Likelihood Ratio statistic is often reported to be the preferred choice as compared to the ‘traditional’ Wald statistic. In this work, we consider and discuss a wider range of test statistics, including the robust Wald, the Score, and the recently proposed Gradient statistic. We compare all these asymptotically equivalent statistics in terms of interval estimation and hypothesis testing via simulation experiments and analyses of two real datasets. We find out that the Likelihood Ratio statistic does not appear the best inferential device in the Firth penal…

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

2020

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…