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Species distribution models (SDMs) are extensively used to estimate species–environment relationships (SERs) and predict species distribution across space and time. For this purpose, it is key to choose relevant spatial grains for predictor and response variables at the onset of the modelling process. However, environmental variables are often derived from large‐scale climate models at a grain that can be coarser than the one of the response variable. Such area‐to‐point spatial misalignment can bias estimates of SER and jeopardise the robustness of predictions. We used a virtual species approach, running simulations across different levels of area‐to‐point spatial misalignment to seek statistical solutions to this problem. We specifically compared accuracy of SER estimates and predictive performances, assessed across different degrees of spatial heterogeneity in environmental conditions, of three SDMs: a GLM, a spatial GLM and a Berkson error model (BEM) that accounts for fine‐grain environmental heterogeneity within coarse‐grain cells. Only the BEM accurately estimates SER from relatively coarse‐grain environmental data (up to 50 times coarser than the response grain), while the two GLMs provide flattened SER. However, all three models perform poorly when predicting from coarse‐grain data, particularly in environments that are more heterogeneous than the training conditions. Conversely, decreasing environmental heterogeneity relative to the training dataset reduces the predictive biases. Because predictions are made from covariate‐grain data, the BEM displays lower predictive performance than the two GLMs. Thus, standard model selection methods would fail to select the model that best estimates SERs (here, the BEM), which could lead to false interpretations about the environmental drivers of species distributions. Overall, we conclude that the BEM, because it can robustly estimate SER at the response grain, holds great promise to overcome area‐to‐point misalignment

Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. We therefore propose two deep learning models for correction and unmixing, which can account for the nonlinear effects and produce more accurate estimates of abundances. Both models use an autoencoder-like architecture to process the captured spectra. One is trained with protoporphyrin IX (PpIX) concentration labels. The other undergoes semi-supervised training, first learning hyperspectral unmixing self-supervised and then learning to correct fluorescence emission spectra for heterogeneous optical and geometric properties using a reference white-light reflectance spectrum in a few-shot manner. The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0.997 and 0.990, respectively, whereas the classical approach achieved only 0.93 and 0.82. The semi-supervised approach's R values were 0.98 and 0.91, respectively. On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better removing bright spots of specular reflectance and reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery., Comment: 20 pages, 8 figures, 3 tables - Under Review

High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis and interpretation of the big data produced remains a challenge. Computer-generated acoustic indices potentially provide a biologically meaningful summary of sound, however, temporal autocorrelation, difficulties in statistical analysis of multi-index data and lack of consistency or transferability in different terrestrial environments have hindered the application of those indices in different contexts. To address these issues we investigate the use of time-series motif discovery and random forest classification of multi-indices through two case studies. We use a semi-automated workflow combining time-series motif discovery and random forest classification of multi-index (acoustic complexity, temporal entropy, and events per second) data to categorize sounds in unfiltered recordings according to the main source of sound present (birds, insects, geophony). Our approach showed more than 70% accuracy in label assignment in both datasets. The categories assigned were broad, but we believe this is a great improvement on traditional single index analysis of environmental recordings as we can now give ecological meaning to recordings in a semi-automated way that does not require expert knowledge and manual validation is only necessary for a small subset of the data. Furthermore, temporal autocorrelation, which is largely ignored by researchers, has been effectively eliminated through the time-series motif discovery technique applied here for the first time to ecoacoustic data. We expect that our approach will greatly assist researchers in the future as it will allow large datasets to be rapidly processed and labeled, enabling the screening of recordings for undesired sounds, such as wind, or target biophony (insects and birds) for bio

Background Heterogeneous respiratory system static compliance (CRS) values and levels of hypoxemia in patients with novel coronavirus disease (COVID-19) requiring mechanical ventilation have been reported in previous small-case series or studies conducted at a national level. Methods We designed a retrospective observational cohort study with rapid data gathering from the international COVID-19 Critical Care Consortium study to comprehensively describe CRS—calculated as: tidal volume/[airway plateau pressure-positive end-expiratory pressure (PEEP)]—and its association with ventilatory management and outcomes of COVID-19 patients on mechanical ventilation (MV), admitted to intensive care units (ICU) worldwide. Results We studied 745 patients from 22 countries, who required admission to the ICU and MV from January 14 to December 31, 2020, and presented at least one value of CRS within the first seven days of MV. Median (IQR) age was 62 (52–71), patients were predominantly males (68%) and from Europe/North and South America (88%). CRS, within 48 h from endotracheal intubation, was available in 649 patients and was neither associated with the duration from onset of symptoms to commencement of MV (p = 0.417) nor with PaO2/FiO2 (p = 0.100). Females presented lower CRS than males (95% CI of CRS difference between females-males: − 11.8 to − 7.4 mL/cmH2O p < 0.001), and although females presented higher body mass index (BMI), association of BMI with CRS was marginal (p = 0.139). Ventilatory management varied across CRS range, resulting in a significant association between CRS and driving pressure (estimated decrease − 0.31 cmH2O/L per mL/cmH20 of CRS, 95% CI − 0.48 to − 0.14, p < 0.001). Overall, 28-day ICU mortality, accounting for the competing ri

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Climate fluctuations affect a wide range of environmental factors that may impact fish populations with various response mechanisms. For a major part of world fisheries stocks, only abundance time-series are available. Relations between abundance of these species and ocean variables, including long term trends and patterns of seasonal change, remain sparsely documented. The main aim of our work is to determine to what extent effects of inter-annual and seasonal changes in environmental factors can be identified and quantified on a selected time-series of abundance data-set: the striped red mullet population within the Bay of Biscay. 8 Landings per unit of effort (LPUE) for striped red mullet data are compared with environmental covariates extracted from the Copernicus web database. Our strategy is to decompose the patterns of change in fish populations into long-term and seasonal components and to analyse the possible dependency of both components on environmental factors. The overall tendencies are a decrease of the long-term component and a modification of the seasonality, with additional spatial variability. The observed tendencies and seasonal changes are discussed against the spatio-temporal patterns of change in environmental factors. High spatial heterogeneity within the Bay of Biscay was found, both for striped red mullet long-term trends and seasonal changes. Most inter-annual and seasonal changes in striped red mullet abundance can be related to environmental factors. In addition to the well-known annual fluctuation, most coastal sites revealed a bi-annual seasonality possibly related to the recruitment process.

Climate fluctuations affect a wide range of environmental factors that may impact fish populations with various response mechanisms. For a major part of world fisheries stocks, only abundance time-series are available. Relations between abundance of these species and ocean variables, including long term trends and patterns of seasonal change, remain sparsely documented. The main aim of our work is to determine to what extent effects of inter-annual and seasonal changes in environmental factors can be identified and quantified on a selected time-series of abundance data-set: the striped red mullet population within the Bay of Biscay. 8 Landings per unit of effort (LPUE) for striped red mullet data are compared with environmental covariates extracted from the Copernicus web database. Our strategy is to decompose the patterns of change in fish populations into long-term and seasonal components and to analyse the possible dependency of both components on environmental factors. The overall tendencies are a decrease of the long-term component and a modification of the seasonality, with additional spatial variability. The observed tendencies and seasonal changes are discussed against the spatio-temporal patterns of change in environmental factors. High spatial heterogeneity within the Bay of Biscay was found, both for striped red mullet long-term trends and seasonal changes. Most inter-annual and seasonal changes in striped red mullet abundance can be related to environmental factors. In addition to the well-known annual fluctuation, most coastal sites revealed a bi-annual seasonality possibly related to the recruitment process.

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square.

Introduction: There is a paucity of data that can be used to guide the management of criticallyill patients with COVID-19. In response, a research and data-sharing collaborative - The COVID-19 Critical Care Consortium - has been assembled to harness the cumulative experience of intensive careunits (ICUs) worldwide. The resulting observational study provides a platform to rapidly disseminate detailed data and insights crucial to improving outcomes. Methods and analysis: This is an international, multicentre, observational study of patients with confirmed or suspected SARS-CoV-2 infection admitted to ICUs. This is an evolving, open-ended study that commenced on 1 January 2020 and currently includes >350 sites in over 48 countries. The study enrols patients at the time of ICU admission and follows them to the time of death, hospital discharge or 28 days post-ICU admission, whichever occurs last. Key data, collected via an electronic case report form devised in collaboration with the International Severe Acute Respiratory and Emerging Infection Consortium/Short Period Incidence Study of Severe Acute Respiratory Illness networks, include: patient demographic data and risk factors, clinical features, severity of illness and respiratory failure, need for non-invasive and/or mechanical ventilation and/or extracorporeal membrane oxygenation and associated complications, as well as data on adjunctive therapies. Ethics and dissemination: Local principal investigatorswill ensure that the study adheres to all relevant national regulations, and that the necessary approvals are in place before a site may contribute data. In jurisdictions where a waiver of consent is deemed insufficient, prospective, representative or retrospective consent will be obtained, as appropriate. A web-based dashboard has been developed to provide relevant data and descriptive statistics to international collaborators in real-time. It is anticipated that, fo

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching t

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square.

Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial neural networks that differed in both learning method and hyperparameter values, then calibrated models using a Bayesian multiobjective optimization procedure, and selected and evaluated the "best"model for each water-quality variable, environment, and anomaly type. We found that semi-supervised classification was better able to detect sudden spikes, sudden shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predicting long-term anomalies associated with drifts and periods of otherwise unexplained high variability.

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square.

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square.

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square.

Objectives: We sought to evaluate the impact of later menarche on the risk of operative delivery. Population: We studied 38,069 eligible women (first labors at term with a singleton infant in a cephalic presentation) from the Norwegian Mothers and Child Cohort Study. The main exposures were the age at menarche and the duration of the interval between menarche and the first birth. Methods: Poisson’s regression with a robust variance estimator. Main outcome measures: Operative delivery, defined as emergency cesarean or assisted vaginal delivery (ventouse extraction or forceps). Results: A 5 year increase in age at menarche was associated with a reduced risk of operative delivery (risk ratio [RR] 0.84, 95%CI 0.78, 0.89; p <.001). Adjustment for the age at first birth slightly strengthened the association (RR 0.79, 95%CI 0.74, 0.84; p <.001). However, the association was lost following adjustment for the menarche to birth interval (RR 0.99, 95%CI 0.93, 1.06; p =.81). A 5 years increase in menarche to birth interval was associated with an increased risk of operative delivery (RR 1.26, 95%CI 1.23, 1.28; p <.001). This was not materially affected by adjustment for an extensive series of maternal characteristics (RR 1.23, 95%CI 1.20, 1.25; p <.001). Conclusions: Later menarche reduces the risk of an operative first birth through shortening the menarche to birth interval. This observation is consistent with the hypothesis that the pattern and/or duration of prepregnancy exposure of the uterus to estrogen and progesterone contributes to uterine aging.

In this paper, we investigate the objective function and deflation process for sparse Partial Least Squares (PLS) regression with multiple components. While many have considered variations on the objective for sparse PLS, the deflation process for sparse PLS has not received as much attention. Our work highlights a flaw in the Statistically Inspired Modification of Partial Least Squares (SIMPLS) deflation method when applied in sparse PLS regression. We also consider the Nonlinear Iterative Partial Least Squares (NIPALS) deflation in sparse PLS regression. To remedy the flaw in the SIMPLS method, we propose a new sparse PLS method wherein the direction vectors are constrained to be sparse and lie in a chosen subspace. We give insight into this new PLS procedure and show through examples and simulation studies that the proposed technique can outperform alternative sparse PLS techniques in coefficient estimation. Moreover, our analysis reveals a simple renormalization step that can be used to improve the estimation of sparse PLS direction vectors generated using any convex relaxation method.

This study focuses on flooding hazards along the Grande Plage of Biarritz. This pocket engineered beach, located in the south west of France, was highly battered by a series of storms during the winter 2013–2014 and experienced great damages to buildings and infrastructures. The vulnerability of this type of beaches to storm impact is assessed using the XBeach surf beat model. For validation purpose, a dedicated spectral wave model is also used on the coastal area to force XBeach at 15 m depth. This coastal wave model is first validated against nearshore wave measurements provided by two sensors deployed during the winter 2017. Then the total water level elevations measured at the shore with a video monitoring system are used to assess XBeach results. The model is then used to run a series of extreme storm scenarios with different return periods (1, 10 and 50 years). Results show how the occurrence of flooding events increases with storm intensity. The response of the beach is alongshore non-uniform for all the storm scenarios. The section, where the dry beach width is smaller, is more exposed to flooding whereas vulnerability is reduced by wider dry beach areas. This study constitutes a milestone and outlines next steps toward the development of an operational warning system for the GPB.

Integrative analysis of high dimensional omics datasets has been studied by many authors in recent years. By incorporating prior known relationships among the variables, these analyses have been successful in elucidating the relationships between different sets of omics data. In this article, our goal is to identify important relationships between genomic expression and cytokine data from a human immunodeficiency virus vaccine trial. We proposed a flexible partial least squares technique, which incorporates group and subgroup structure in the modelling process. Our new method accounts for both grouping of genetic markers (eg, gene sets) and temporal effects. The method generalises existing sparse modelling techniques in the partial least squares methodology and establishes theoretical connections to variable selection methods for supervised and unsupervised problems. Simulation studies are performed to investigate the performance of our methods over alternative sparse approaches.

Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.

Free to read on publisher website Pathway analysis can increase power to detect associations with a gene or a pathway by combining several signals at the single nucleotide polymorphism (SNP)-level into a single test. In this work, we propose to extend two well-known self-contained methods, the Fisher’s method (FM) and the Adaptive Rank Truncated Product (ARTP) method to the analysis of gene-environment (GxE) interaction at the gene and pathway-level. It has been previously suggested that the permutation procedures that are usually used to derive the significance of these tests are not appropriate for the analysis of GxE interaction and should be replaced by a bootstrap approach. We analyse and compare the performance of the extension of FM and ARTP using the permutation and the parametric bootstrap procedure in simulation studies. We illustrate its application by analysing the interaction between night work and circadian gene polymorphisms in the risk of breast cancer in a case-control study. The ARTP method, adapted for both gene- and pathway-environment interactions, gives promising results and has been wrapped to the R package PIGE available on the CRAN.

Ce livre est consacré à un outil désormais incontournable pour l’analyse de données, l’élaboration de graphiques et le calcul statistique : le logiciel R. Après avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des données, importation et exportation, accès à la documentation, représentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage détaillent l'ensemble des manipulations permettant la manipulation avec R d'un très grand nombre de méthodes et de notions statistiques : simulation de variables aléatoires, intervalles de confiance, tests d'hypothèses, valeur-p, bootstrap, régression linéaire, ANOVA (y compris répétées), et d'autres encore. Écrit avec un grand souci de pédagogie et clarté, agrémenté de nombreux exercices et travaux pratiques, ce livre accompagnera idéalement tous les utilisateurs de R- et ceci sur les environnements Windows, Macintosh ou Linux - qu'ils soient débutants ou d'un niveau avancé.

We propose two multivariate extensions of the Bayesian group lasso for variable selection and estimation for data with high dimensional predictors and multi-dimensional response variables. The methods utilize spike and slab priors to yield solutions which are sparse at either a group level or both a group and individual feature level. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between multiple responses and predictors. The approach is suited to many biological studies where the response is multivariate and each predictor is embedded in some biological grouping structure such as gene pathways. Our Bayesian models are connected with penalized regression, and we prove both oracle and asymptotic distribution properties under an orthogonal design. We derive efficient Gibbs sampling algorithms for our models and provide the implementation in a comprehensive R package called MBSGS available on the Comprehensive R Archive Network (CRAN). The performance of the proposed approaches is compared to state-of-the-art variable selection strategies on simulated data sets. The proposed methodology is illustrated on a genetic dataset in order to identify markers grouping across chromosomes that explain the joint variability of gene expression in multiple tissues.

Technological advances in molecular biology over the past decade have given rise to high dimensional and complex datasets offering the possibility to investigate biological associations between a range of genomic features and complex phenotypes. The analysis of this novel type of data generated unprecedented computational challenges which ultimately led to the definition and implementation of computationally efficient statistical models that were able to scale to genome-wide data, including Bayesian variable selection approaches. While extensive methodological work has been carried out in this area, only few methods capable of handling hundreds of thousands of predictors were implemented and distributed. Among these we recently proposed GUESS, a computationally optimised algorithm making use of graphics processing unit capabilities, which can accommodate multiple outcomes. In this paper we propose R2GUESS, an R package wrapping the original C++ source code. In addition to providing a user-friendly interface of the original code automating its parametrisation, and data handling, R2GUESS also incorporates many features to explore the data, to extend statistical inferences from the native algorithm (e.g., effect size estimation, significance assessment), and to visualize outputs from the algorithm. We first detail the model and its parametrisation, and describe in details its optimised implementation. Based on two examples we finally illustrate its statistical performances and flexibility.

Free to read on publisher website Motivation: The association between two blocks of ‘omics’ data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. Results: We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine. Availability and implementation: The approach is implemented in a comprehensive R package called sgPLS available on the CRAN.

In humans, the risk of operative first delivery increases linearly with maternal age. We previously hypothesized that prolonged, cyclical, prepregnancy exposure to estrogen and progesterone contributes to uterine aging. Here, we test this hypothesis. Myometrium was obtained from four groups of virgin mice: (i) 10‐ to 12‐week‐ and 28‐ to 30‐week‐old mice; (ii) 10‐ to 12‐week‐ and 38‐ to 40‐week‐old mice; (iii) 38‐week‐old mice that had an ovariectomy or sham operation early in life; (iv) 38‐week‐old mice that had been treated with progesterone or vehicle containing implants from 8 to 36 weeks. Transcript profiling was carried out using Affymetrix Gene ST 1.1 arrays, and data were normalized. We identified 60 differentially regulated transcripts associated with advancing age (group 1). We validated these changes in group 2 (P for overlap=5.8×10−46). Early ovariectomy prevented the age‐related changes in myometrial transcript profile. Similarly, progesterone‐mediated long‐term ovarian suppression prevented the age‐related changes in myometrial transcript profile. Interferon regulatory factor 7 (Irf7) mRNA was regulated by age and hormonal exposure, and was identified as a predicted regulator of the other differentially expressed transcripts by both promoter sequence and canonical pathway activation analysis (P=8.47×10−5 and P10−10, respectively). Immunohistochemistry demonstrated IRF7 in both mouse and human myometrium. We conclude the following: (i) Myometrial aging in mice is associated with reproducible changes in transcript profile; (ii) these changes can be prevented by interventions which inhibit cyclical changes in the female sex hormones; and (iii) IRF7 may be an important regulator of myometrial function and aging.

"Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results."--Publisher website

"Cet ouvrage fait le point sur les méthodes disponibles à ce jour pour analyser les données longitudinales. Les principaux résultats de l'inférence statistique et les principaux modèles de régression sont rappelés. Le reste du livre se concentre sur les modèles pour données longitudinales au sens large, c'est-à-dire mesures répétées de variables quantitatives ou qualitatives et histoire d'événements. L'ouvrage est accompagné par le développement d'un ensemble de logiciels R. Il s'agit d'un livre collectif, fruit de l'expérience de recherche et d'enseignement d'une équipe dont les membres ont travaillé ensemble depuis des années. Grâce à cette expérience commune des neuf auteurs, le livre est homogène à la fois dans la vision générale et dans les notations."--Publisher website

本书是作者根据近年来在法国格勒诺布尔第二大学统计与商业智能系开设的一系列R课程的讲义提炼和精选而成，在Springer出版了法语版和英文版。 本书内容丰富详实，不仅涵盖了R语言绝大部分的基本操作命令，还由浅入深地介绍了数学和统计领域相当多的教学内容和方法在R中的编程实现，并独具匠心以一种易被读者接受和消化的方式来编排和组织。本书给出了几个实际数据集供读者自由下载，利用这些数据集在R软件中进行各种统计方法的演示。另外，本书还穿插介绍了作者多年在R语言编程方面的一些心得和技巧。 本书的大部分内容适用于各种操作系统（如主流的Windows、Linux、Mac）的用户，既可用作R初学者的教材或自学材料，对高级学者和研究人员也有参考价值。本书的全部习题和实践操作的答案在该书的网站上提供下载服务。 Translation: This book is based on the refinement and selection of a series of R courses offered by the Department of Statistics and Business Intelligence at the University of Grenoble in France in recent years. It is published in French and English in Springer. The content of this book is rich and detailed, which not only covers most of the basic operation commands of R language, but also introduces the programming content and method of quite a lot of teaching content and methods in mathematics and statistics field in R, and it is unique and easy to use. Organized and organized by readers to accept and digest. This book gives several practical data sets for readers to download freely, using these data sets to demonstrate various statistical methods in R software. In addition, the book also interspersed with the author's many years of experience and skills in R language programming. Most of the contents of this book are applicable to users of various operating systems (such as mainstream Windows, Linux, Mac). They can be used as textbooks or self-learning materials for R beginners, and also have reference value for advanced scholars and researchers. The answers to all the exercises and practical exercises in this book are available on the book's website.

"Cet ouvrage fait le point sur les méthodes disponibles à ce jour pour analyser les données longitudinales. Les principaux résultats de l'inférence statistique et les principaux modèles de régression sont rappelés. Le reste du livre se concentre sur les modèles pour données longitudinales au sens large, c'est-à-dire mesures répétées de variables quantitatives ou qualitatives et histoire d'événements. L'ouvrage est accompagné par le développement d'un ensemble de logiciels R. Il s'agit d'un livre collectif, fruit de l'expérience de recherche et d'enseignement d'une équipe dont les membres ont travaillé ensemble depuis des années. Grâce à cette expérience commune des neuf auteurs, le livre est homogène à la fois dans la vision générale et dans les notations."--Publisher website

We derive an ordinary differential equation (ODE) for solving moment matching problems of truncated univariate distributions. Our method produces a trajectory that solves a family of moment matching problems for different truncation values all with the same target moments.

Ce livre est consacré à un outil désormais incontournable pour l’analyse de données, l’élaboration de graphiques et le calcul (bio)statistique : le logiciel R. La lecture de l'ouvrage débute par une présentation des possibilités offertes par le logiciel au travers d'une prise en main rapide via l'outil d'interface graphique RCommander, puis via la console. Après avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des données, importation et exportation, accès à la documentation, représentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage détaillent l’ensemble des manipulations permettant la manipulation avec R d’un très grand nombre de méthodes et de notions statistiques : simulation de variables aléatoires, intervalles de confiance, tests d’hypothèses, valeur-p, bootstrap, régression linéaire, ANOVA (y compris répétées), et d’autres encore. Écrit avec un grand souci de pédagogie et clarté, et agrémenté de nombreux exercices et travaux pratiques dont la correction est maintenant disponible à la fois à la fin de l'ouvrage et au téléchargement sur le site web associé, ce livre accompagnera idéalement tous les utilisateurs de R – et cela sur les environnements Windows, Macintosh ou Linux – qu’ils soient débutants ou d’un niveau avancé : étudiants, enseignants ou chercheurs en statistique, mathématiques, médecine, informatique, biologie, psychologie, sciences infirmières, etc. Il leur permettra de maîtriser en profondeur le fonctionnement de ce logiciel. L’ouvrage sera aussi utile aux utilisateurs plus confirmés qui retrouveront exposé ici l’ensemble des fonctions R les plus couramment utilisées, ainsi que les techniques permettant l'interfaçage avec les langages de plus bas niveau C/C++ et Fortran, et une initiation au calcul parallèle et sur carte graphique.

The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced. This book can serve as a textbook on R for beginners as well as more advanced users, working on Windows, MacOs or Linux OSes. The first part of the book deals with the heart of the R language and its fundamental concepts, including data organization, import and export, various manipulations, documentation, plots, programming and maintenance. The last chapter in this part deals with oriented object programming as well as interfacing R with C/C++ or Fortran, and contains a section on debugging techniques. This is followed by the second part of the book, which provides detailed explanations on how to perform many standard statistical analyses, mainly in the Biostatistics field. Topics from mathematical and statistical settings that are included are matrix operations, integration, optimization, descriptive statistics, simulations, confidence intervals and hypothesis testing, simple and multiple linear regression, and analysis of variance. Each statistical chapter in the second part relies on one or more real biomedical data sets, kindly made available by the Bordeaux School of Public Health (Institut de Santé Publique, d'Épidémiologie et de Développement - ISPED) and described at the beginning of the book. Each chapter ends with an assessment section: memorandum of most important terms, followed by a section of theoretical exercises (to be done on paper), which can be used as questions for a test. Moreover, worksheets enable the reader to check his new abilities in R. Solutions to all exercises and worksheets are included in this book.

Background High throughput ’omics’ experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects. Results We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effects of conditions within subjects from the biological variation between subjects. The approach is extended to two-factor designs and to the integration of two matched data sets. It allows internal variable selection to highlight genes able to discriminate the net condition effect within subjects. A simulation study was performed to demonstrate the good performance of the multilevel multivariate approach compared to a classical multivariate method. The multilevel multivariate approach outperformed the classical multivariate approach with respect to the classification error rate and the selection of relevant genes. The approach was applied to an HIV-vaccine trial evaluating the response with gene expression and cytokine secretion. The discriminant multilevel analysis selected a relevant subset of genes while the integrative multilevel analysis highlighted clusters of genes and cytokines that were highly correlated across the samples. Conclusions Our combined multilevel multivariate approach may help in finding signatures of vaccine effect and allows for a better understanding of immunological mechanisms activated by the intervention. The integrative analysis revealed clusters of genes, that were associated with cytokine secretion. These clusters can be seen as gene signatures to predict future cytokine response. The approach is implemented in the R package mixOmics (http://cran.r-project.org/) with associated tutorials to perform the analysisa.

Attributable risk has become an important concept in clinical epidemiology. In this paper, we suggest to estimate the attributable risk of nosocomial infections using a multistate approach. Recently, a multistate model (called progressive disability model in the literature) has been developed in order to take into consideration both the time‐dependency of the risk factor (e.g., nosocomial infections) and the presence of competing risks (e.g., death and discharge) at each time point. However, this approach does not take into account the possible heterogeneity of the study population. In this paper, we investigate an extension of this model and suggest an adjusted disability multistate model including covariates in each transition. This new multistate model has led us to define the concepts of overall and profiled attributable risk. We use a classical semiparametric approach to estimate the model and the new attributable risk. A simulation study is investigated and we show, in particular, that neglecting the presence of covariates when estimating the model can lead to an important bias. The methodology developed in this paper is applied to data on ventilator‐associated pneumonia in 12 French intensive care units.

A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function over all non-missing elements, which can be achieved by EM-type algorithms where an iterative imputation of the missing values is performed during the estimation of the axes and components. This paper proposes such an algorithm, named iterative multiple correspondence analysis, to handle missing values in multiple correspondence analysis (MCA). The algorithm, based on an iterative PCA algorithm, is described and its properties are studied. We point out the overfitting problem and propose a regularized version of the algorithm to overcome this major issue. Finally, performances of the regularized iterative MCA algorithm (implemented in the R-package named missMDA) are assessed from both simulations and a real dataset. Results are promising with respect to other methods such as the missing-data passive modified margin method, an adaptation of the missing passive method used in Gifi’s Homogeneity analysis framework.

Sliced inverse regression (SIR) and related methods were introduced in order to reduce the dimensionality of regression problems. In general semiparametric regression framework, these methods determine linear combinations of a set of explanatory variables X related to the response variable Y, without losing information on the conditional distribution of Y given X. They are based on a “slicing step” in the population and sample versions. They are sensitive to the choice of the number H of slices, and this is particularly true for SIR-II and SAVE methods. At the moment there are no theoretical results nor practical techniques which allows the user to choose an appropriate number of slices. In this paper, we propose an approach based on the quality of the estimation of the effective dimension reduction (EDR) space: the square trace correlation between the true EDR space and its estimate can be used as goodness of estimation. We introduce a naïve bootstrap estimation of the square trace correlation criterion to allow selection of an “optimal” number of slices. Moreover, this criterion can also simultaneously select the corresponding suitable dimension K (number of the linear combination of X). From a practical point of view, the choice of these two parameters H and K is essential. We propose a 3D-graphical tool, implemented in R, which can be useful to select the suitable couple (H, K). An R package named “edrGraphicalTools” has been developed. In this article, we focus on the SIR-I, SIR-II and SAVE methods. Moreover the proposed criterion can be use to determine which method seems to be efficient to recover the EDR space, that is the structure between Y and X. We indicate how the proposed criterion can be used in practice. A simulation study is performed to illustrate the behavior of this approach and the need for selecting properly the num

Background Multistate models have become increasingly useful to study the evolution of a patient’s state over time in intensive care units ICU (e.g. admission, infections, alive discharge or death in ICU). In addition, in critically-ill patients, data come from different ICUs, and because observations are clustered into groups (or units), the observed outcomes cannot be considered as independent. Thus a flexible multi-state model with random effects is needed to obtain valid outcome estimates. Methods We show how a simple multi-state frailty model can be used to study semi-competing risks while fully taking into account the clustering (in ICU) of the data and the longitudinal aspects of the data, including left truncation and right censoring. We suggest the use of independent frailty models or joint frailty models for the analysis of transition intensities. Two distinct models which differ in the definition of time t in the transition functions have been studied: semi-Markov models where the transitions depend on the waiting times and nonhomogenous Markov models where the transitions depend on the time since inclusion in the study. The parameters in the proposed multi-state model may conveniently be computed using a semi-parametric or parametric approach with an existing R package FrailtyPack for frailty models. The likelihood cross-validation criterion is proposed to guide the choice of a better fitting model. Results We illustrate the use of our approach though the analysis of nosocomial infections (ventilator-associated pneumonia infections: VAP) in ICU, with “alive discharge” and “death” in ICU as other endpoints. We show that the analysis of dependent survival data using a multi-state model without frailty terms may underestimate the variance of regression coefficients specific to each group, leading to incorrect inferences. Some factors are wrongly significantly associated based on the model without frailty terms. This result is confirmed

Prognostic estimators for a clinical event may use repeated measurements of markers in addition to fixed covariates. These measurements can be linked to the clinical event by joint models that involve latent features. When the objective is to choose between different prognosis estimators based on joint models, the conventional Akaike information criterion is not well adapted and decision should be based on predictive accuracy. We define an adapted risk function called expected prognostic cross‐entropy. We define another risk function for the case of right‐censored observations, the expected prognostic observed cross‐entropy (EPOCE). These risks can be estimated by leave‐one‐out cross‐validation, for which we give approximate formulas and asymptotic distributions. The approximated cross‐validated estimator CVPOLa of EPOCE is studied in simulation and applied to the comparison of several joint latent class models for prognosis of recurrence of prostate cancer using prostate‐specific antigen measurements.

Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing ``as good as possible'' the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two clustering algorithms are proposed to optimize the homogeneity criterion: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets.

Ce livre est consacré à un outil désormais incontournable pour l’analyse de données, l’élaboration de graphiques et le calcul statistique : le logiciel R. Après avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des données, importation et exportation, accès à la documentation, représentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage détaillent l’ensemble des manipulations permettant la manipulation avec R d’un très grand nombre de méthodes et de notions statistiques : simulation de variables aléatoires, intervalles de confiance, tests d’hypothèses, valeur-p, bootstrap, régression linéaire, ANOVA (y compris répétées), et d’autres encore. Écrit avec un grand souci de pédagogie et clarté, et agrémenté de nombreux exercices et travaux pratiques, ce livre accompagnera idéalement tous les utilisateurs de R – et cela sur les environnements Windows, Macintosh ou Linux – qu’ils soient débutants ou d’un niveau avancé : étudiants, enseignants ou chercheurs en statistique, mathématiques, médecine, informatique, biologie, psychologie, sciences infirmières, etc. Il leur permettra de maîtriser en profondeur le fonctionnement de ce logiciel. L’ouvrage sera aussi utile aux utilisateurs plus confirmés qui retrouveront exposé ici l’ensemble des fonctions R les plus couramment utilisées.

A criterion for choosing an estimator in a family of semi-parametric estimators from incomplete data is proposed. This criterion is the expected observed log-likelihood (ELL). Adapted versions of this criterion in case of censored data and in presence of explanatory variables are exhibited. We show that likelihood cross-validation (LCV) is an estimator of ELL and we exhibit three bootstrap estimators. A simulation study considering both families of kernel and penalized likelihood estimators of the hazard function (indexed on a smoothing parameter) demonstrates good results of LCV and a bootstrap estimator called ELLbboot. When using penalized likelihood an approximated version of LCV also performs very well. The use of these estimators of ELL is exemplified on the more complex problem of choosing between stratified and unstratified proportional hazards models. An example is given for modeling the effect of sex and educational level on the risk of developing dementia.