1,721,064 research outputs found
Graphical chain models for the analysis of complex genetic diseases: an application to hypertension
No full textA crucial task in modern genetic medicine is the understanding of complex genetic diseases. The
main complicating features are that a combination of genetic and environmental risk factors is involved,
and the phenotype of interest may be complex. Traditional statistical techniques based on lod-scores fail
when the disease is no longer monogenic and the underlying disease transmission model is not defined.
Different kinds of association tests have been proved to be an appropriate and powerful statistical tool to detect a ‘candidate gene’ for a complex disorder. However, statistical techniques able to nvestigate direct and indirect influences among phenotypes, genotypes and environmental risk factors, are required to
analyse the association structure of complex diseases. In this paper, we propose graphical models as a
natural tool to analyse the multifactorial structure of complex genetic diseases. An application of this
model to primary hypertension data set is illustrated
Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course
Full text linkThis paper aims at proposing suitable statistical tools to address heterogeneity in repeated measures, within a Multiple Sclerosis (MS) longitudinal study. Indeed, due to unobservable sources of heterogeneity, modelling the effect of covariates on MS severity evolves as a very difficult feature. Bayesian P-Splines are suggested for modelling non linear smooth effects of covariates within generalized additive models. Thus, based on a pooled MS data set, we show how extending Bayesian P-splines to mixed effects models (Lang and Brezger, 2001), represents an attractive statistical approach to investigate the role of prognostic factors in affecting individual change in disability
Graphical chain models for the analysis of complex genetic diseases: an application to hypertension
Full text linkA crucial task in modern genetic medicine is the understanding of complex genetic diseases. The main complicating features are that a combination of genetic and environmental risk factors is involved, and the phenotype of interest may be complex. Traditional statistical techniques based on lod-scores fail when the disease is no longer monogenic and the underlying disease transmission model is not defined. Different kinds of association tests have been proved to be an appropriate and powerful statistical tool to detect a candidate gene for a complex disorder. However, statistical techniques able to investigate direct and indirect influences among phenotypes, genotypes and environmental risk factors, are required to analyse the association structure of complex diseases. In this paper we propose graphical models as a natural tool to analyse the multifactorial structure of complex genetic diseases. An application of this model to primary hypertension data set is illustrated
Endometrial metastasis of a primitive neuroendocrine ovarian carcinoma: management and treatment of a case.
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Comparing fundraising campaigns in healthcare using psychophysiological data: a network-based approach
No full textMeasuring the effectiveness of fundraising campaigns is crucial for improving communication strategies. This is particularly pertinent for healthcare campaigns aimed at raising awareness about sensitive health issues that require financial support for advancing research efforts. The present work assesses campaign effectiveness by examining brain activation evoked by different video stimuli. Within a multivariate statistical setting, we compare the physiological responses that are induced by four fundraising campaigns designed under different communication strategies. Specifically, we model attention-related electroencephalographic (EEG) signals using graphical models to estimate partial correlation networks associated with each video campaign. These networks are then compared in terms of structure and connectivity using resampling methods. The proposed approach is flexible, allowing for the analysis of induced physiological responses at both local and global levels. It accounts for the interrelationships among collected EEG data and participants’ heterogeneity, overcoming the need to derive composite scores as is commonly done in neuromarketing research areas. The networks derived from different campaigns exhibit significantly different structures and connectivity, indicating distinct cognitive and emotional responses induced by the videos. Given its generality, our proposed approach can be applied effectively in psychological and neuroscientific research fields whenever the physiological response to affective stimuli is of interest. Graphical abstract: (Figure presented.)
Unusual case of cervical pregnancy after curettage for a presumptive diagnosis of intrauterine blighted ovum.
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