Showing results 121 to 140 on 1814 in total
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Longitudinal studies of disease progression and treatment increasingly involve time-varying treatments. Many such treatments may have cumulative effects, where the risk of the outcome does depend not only on the current or most recent treatment status or dose but also on the history of the past treatment. One important analytical challenge in such studies concerns the need to specify an 'etiologically correct exposure metric' that summarizes the impact of treatment/exposure history on the current hazard. Flexible modeling of a weighted cumulative exposure (WCE), where the exposure metric is defined as a weighted sum of past treatments, has been proposed to address this challenge and the WCE model has been shown to incorporate conventional simpler exposure models as its special cases. Another important challenge in assessing the causal effects of time-varying treatments occurs if the treatment both affects (future) and depends on (past) values of a time-varying risk factor. Such risk factor will act then as both a confounder and a mediator of the estimated treatment effect. Marginal Structural Models (MSM) have been developed and demonstrated to provide un-biased treatment effect estimates in the presence of such time-varying confounders/mediators. We propose, and validate in simulations, a new, flexible model that combines the MSM and the WCE methodologies. The new model is a flexible extension of the weighted Cox MSM, with inverse-probability of treatment (IPT) weights. To estimate the cumulative effect of the past treatments, we use use cubic regression splines to estimate the marginal weight function, which estimates the relative importance weights assigned to the past exposures, depending on the time elapsed since the exposure. The new WCE model is implemented by inserting the artificial time-dependent (TD) covariates into the Cox model. Stabilized IPT TD weights are employed to control for TD confounders / mediators of the treatment effect. Simulations demonstrate that our MSM WCE estimates well capture the total causal effect of time-varying treatments i.e. the sum of (i) its direct effect on the hazard, and (ii) its indirect effect, mediated through changes in the TD confounder/mediator. Furthermore, if the indirect effect is moderate or strong, the estimated marginal cumulative treatment effect may be substantially stronger than the effect estimate from the conventional (un-weighted) 'conditional' WCE model. Xiao Y, Abrahamowicz M, Moodie EEM, Weber R, Young J. Flexible Marginal Structural Models for Estimating the Cumulative Effect of a Time-Dependent Treatment on the Hazard: Reassessing the Cardiovascular Risks of Didanosine Treatment in the Swiss HIV Cohort Study. Journal of the American Statistical Association. Jan 2014; Epub [DOI: 10.1080/01621459.2013.872650] Merci de me confirmer votre présence avant le jeudi 17 décembre 2015 en raison des congés de fin d'année Cordialement.
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Several protein isoforms can be produced from a single gene through alternative splicing. These isoforms have different protein sequences, and often diverse or even antagonistic functions. In the recent years,the use of high-throughput technologies has revealed that alternative splicing is massively deregulated in many experimental conditions. A proportion of the splicing events observed at the transcript level are also observed at the protein level. However, it is still difficult to decipher the functional consequences of these splicing variations because of the lack of functional information at the exon level. To circumvent that problem, we introduce a computational strategy that relies on the functional annotation of exons in order to predict the consequences of their inclusion or skipping.
Du fait du manque d'outils spécifiques, les données ordinales sont souvent assimilées soit à des données nominales, oubliant la notion d'ordre, soit à des données quantitatives, introduisant artificiellement une notion de distance entre modalités. Dans le but d'éviter l'utilisation d'une de ces deux solutions extrêmes, nous proposons une nouvelle distribution de probabilité pour données ordinales, paramétrée par un paramètre de position et un paramètre de précision. Cette distribution est ensuite utilisée pour définir un algorithme de clustering spécifique aux données ordinales, permettant de prendre en compte les données multivariées et potentiellement manquantes. Cet algorithme a été implémenté dans des solutions logiciels (package R, logiciel en ligne SaaS), qui seront utilisés pour une démonstration sur données réelles.Contact : Mme Mariethé CHAUMEIL Inscription gratuite mais obligatoire au plus tard le lundi 29 février 2016 Courriel : mariethe.chaumeil@chu-lyon.fr
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To meet the Water Framework Directive requirements, a new multimetric index (I2M2) has been designed for the invertebrate-based ecological assessment of French wadeable streams. Integrating five taxonomy- and trait-based metrics selected for their high discrimination efficiency, low pressure specificity, high stability in least impaired conditions and low redundancy, this index is meant to identify impaired reaches for 17 anthropogenic pressure categories potentially impairing water quality or habitat. Based on I2M2, any river reach is assigned an ecological quality class among "Bad", "Poor", "Moderate", "Good" and "High". I will present the I2M2, the strategy underlying its construction and on-going developments to help local managers for restoration and conservation programs: (i) assessment of the uncertainty associated to the I2M2 to make it a probabilistic ecological indicator, (ii) development of diagnostic tools based on conditional tree forest models to estimate the risk that each pressure category occurred at any river reach from sampled community traits (iii) simulation of reference conditions for large streams
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Multicellular organisms are morphologically very diverse at every scale, regarding size, color, and shape of individuals and of their different parts. Natural selection and developmental constraints influence evolution of these characteristics, on the short term as well as on the long term. The model chosen here to study form evolution is the pollen grain of flowering plants, which is much diversified morphologically. We focused on specific features called apertures, which are structures of the pollen wall involved in survival and reproduction. We investigated the relative contributions of selection and constraints in a clade representing about 75% of extant species, and we showed that the dominant pattern of this group could constitute a good trade-off between survival and reproduction components of fitness.
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