Atouts et faiblesses du logiciel R en enseignement, recherche et industrie

International audienceDans cette conférence, nous aborderons le logiciel R sous trois aspects : son utilisation en enseignement, en recherche et dans le monde de l'entreprise. Pour chacune de ces trois utilisations, il est nécessaire d'évaluer les demandes spécifiques du domaine et les réponses qu'apporte le logiciel R. Rappelons brièvement que le logiciel R est multi-plateforme et multi-OS. Il est entièrement gratuit, très complet et offre à la fois des commandes mais aussi des menus déroulants. Il est donc raisonnable de penser que ce logiciel fera partie des logiciels de statistique les plus enseignés. Sa facilité de programmation et sa forte utilisation dans le monde de la recherche en font dès aujourd'hui un language omniprésent. On peut donc s'interroger sur les futures évolutions de R vis à vis de la recherche en statistiques. Dans la troisième partie, nous comparerons R avec ses différents concurrents et analyserons les points qui gouvernent les choix de logiciels en entreprise (prix, interface graphique, intégration dans les base de données...). Bien évidemment le logiciel est loin d'être parfait mais il comporte dès à présent des avantages qui lui valent d'être adopté par un nombre croissant d'entreprises

Réduction itérative du biais pour des lisseurs multivariés

International audienceLa méthode IBR (iterated biased reduction) permet d'estimer une fonction de régression $m$ inconnue lorsque les variables explicatives sont à valeurs dans \mathbbR^d. Pour estimer la fonction $m$, les méthodes non-paramétriques classiques souffrent du fléau de la dimension. En pratique, il faut donc supposer des hypothèses structurelles: modèles additifs, modèles à directions révélatrices... A contrario IBR estime directement la fonction de régression $m$. Elle concurrence MARS, les directions révélatrices ou les modèles additifs et sur des exemples réels ou simulés et elle apporte des gains significatifs sur l'erreur de prévision. Cette méthode utilise en pratique un lisseur pilote soit de type splines plaque-minces soit de type noyau gaussien. Cet estimateur pilote est utilisé de manière répétée afin d'estimer le biais et permet de l'enlever progressivement. La méthode, à l'instar du $L_2$ boosting, nécessite donc l'estimation de l'itération optimale. Des résultats de vitesse de convergence (vitesse minimax) de l'erreur quadratique moyenne de l'estimateur (avec itération optimale) ont été obtenus. L'optimalité du critère de choix de l'itération (GCV) a aussi été démontré. Un exemple simulé simple ($d=2$) et un exemple réel ($d=8$) seront traités et comparés aux méthodes existantes: GAM, MARS, PPR, ou $L_2$-boosting. Un package \textsfR disponible sur le CRAN permet d'utiliser cette méthode très simplement

Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package

In multivariate nonparametric analysis curse of dimensionality forces one to use large smoothing parameters. This leads to a biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting base smoother has a small variance but a substantial bias. In this paper, we propose an R package named ibr to iteratively correct the initial bias of the (base) estimator by an estimate of the bias obtained by smoothing the residuals. After a brief description of iterated bias reduction smoothers, we examine the base smoothers implemented in the package: Nadaraya-Watson kernel smoothers, Duchon splines smoothers and their low rank counterparts. Then, we explain the stopping rules available in the package and their implementation. Finally we illustrate the package on two examples: a toy example in R2 and the original Los Angeles ozone dataset

Prévision de la consommation d'électricité par correction itérative du biais

International audienceUne prévision correcte de la consommation d'électricité est fondamentale pour le bon fonctionnement du réseau électrique français, dont Réseau de Transport d'Electricité a la charge. Les prévisions utilisées quotidiennement par RTE sont issues d'un modèle alliant une régression paramétrique non linéaire et un modèle SARIMA. Dans l'idée d'obtenir un modèle de prévision adaptatif, des méthodes de prévision non-paramétriques ont déjà été testées sans succès véritable. On sait notamment que la qualité d'un prédicteur non-paramétrique résiste mal à un grand nombre de variables explicatives, ce qu'on appelle communément le fléau de la dimension. Nous utilisons une méthode de correction itérative du biais, en lissant les résidus obtenus à chaque étape. Nous appliquons cette méthode à la consommation d'électricité française sur laquelle les performances sont bonnes

Non-parametric lower bounds and information functions

We argue that common features of non-parametric estimation appear in parametric cases as well if there is a deviation from the classical regularity condition. Namely, in many non-parametric estimation problems (as well as some parametric cases) unbiased finite-variance estimators do not exist; neither estimator converges locally uniformly with the optimal rate; there are no asymptotically unbiased with the optimal rate estimators; etc.. We argue that these features naturally arise in particular parametric subfamilies of non-parametric classes of distributions. We generalize the notion of regularity of a family of distributions and present a general regularity condition, which leads to the notions of the information index and the information function. We argue that the typical structure of a continuity modulus explains why unbiased finite-variance estimators cannot exist if the information index is larger than two, while in typical non-parametric situations neither estimator converges locally uniformly with the optimal rate. We present a new result on impossibility of locally uniform convergence with the optimal rate

Natural Killer Cells Promote Early CD8 T Cell Responses against Cytomegalovirus

Understanding the mechanisms that help promote protective immune responses to pathogens is a major challenge in biomedical research and an important goal for the design of innovative therapeutic or vaccination strategies. While natural killer (NK) cells can directly contribute to the control of viral replication, whether, and how, they may help orchestrate global antiviral defense is largely unknown. To address this question, we took advantage of the well-defined molecular interactions involved in the recognition of mouse cytomegalovirus (MCMV) by NK cells. By using congenic or mutant mice and wild-type versus genetically engineered viruses, we examined the consequences on antiviral CD8 T cell responses of specific defects in the ability of the NK cells to control MCMV. This system allowed us to demonstrate, to our knowledge for the first time, that NK cells accelerate CD8 T cell responses against a viral infection in vivo. Moreover, we identify the underlying mechanism as the ability of NK cells to limit IFN-α/β production to levels not immunosuppressive to the host. This is achieved through the early control of cytomegalovirus, which dramatically reduces the activation of plasmacytoid dendritic cells (pDCs) for cytokine production, preserves the conventional dendritic cell (cDC) compartment, and accelerates antiviral CD8 T cell responses. Conversely, exogenous IFN-α administration in resistant animals ablates cDCs and delays CD8 T cell activation in the face of NK cell control of viral replication. Collectively, our data demonstrate that the ability of NK cells to respond very early to cytomegalovirus infection critically contributes to balance the intensity of other innate immune responses, which dampens early immunopathology and promotes optimal initiation of antiviral CD8 T cell responses. Thus, the extent to which NK cell responses benefit the host goes beyond their direct antiviral effects and extends to the prevention of innate cytokine shock and to the promotion of adaptive immunity

Ruxolitinib for Glucocorticoid-Refractory Acute Graft-versus-Host Disease

BACKGROUND: Acute graft-versus-host disease (GVHD) remains a major limitation of allogeneic stem-cell transplantation; not all patients have a response to standard glucocorticoid treatment. In a phase 2 trial, ruxolitinib, a selective Janus kinase (JAK1 and JAK2) inhibitor, showed potential efficacy in patients with glucocorticoid-refractory acute GVHD. METHODS: We conducted a multicenter, randomized, open-label, phase 3 trial comparing the efficacy and safety of oral ruxolitinib (10 mg twice daily) with the investigator's choice of therapy from a list of nine commonly used options (control) in patients 12 years of age or older who had glucocorticoid-refractory acute GVHD after allogeneic stem-cell transplantation. The primary end point was overall response (complete response or partial response) at day 28. The key secondary end point was durable overall response at day 56. RESULTS: A total of 309 patients underwent randomization; 154 patients were assigned to the ruxolitinib group and 155 to the control group. Overall response at day 28 was higher in the ruxolitinib group than in the control group (62% [96 patients] vs. 39% [61]; odds ratio, 2.64; 95% confidence interval [CI], 1.65 to 4.22; P<0.001). Durable overall response at day 56 was higher in the ruxolitinib group than in the control group (40% [61 patients] vs. 22% [34]; odds ratio, 2.38; 95% CI, 1.43 to 3.94; P<0.001). The estimated cumulative incidence of loss of response at 6 months was 10% in the ruxolitinib group and 39% in the control group. The median failure-free survival was considerably longer with ruxolitinib than with control (5.0 months vs. 1.0 month; hazard ratio for relapse or progression of hematologic disease, non-relapse-related death, or addition of new systemic therapy for acute GVHD, 0.46; 95% CI, 0.35 to 0.60). The median overall survival was 11.1 months in the ruxolitinib group and 6.5 months in the control group (hazard ratio for death, 0.83; 95% CI, 0.60 to 1.15). The most common adverse events up to day 28 were thrombocytopenia (in 50 of 152 patients [33%] in the ruxolitinib group and 27 of 150 [18%] in the control group), anemia (in 46 [30%] and 42 [28%], respectively), and cytomegalovirus infection (in 39 [26%] and 31 [21%]). CONCLUSIONS: Ruxolitinib therapy led to significant improvements in efficacy outcomes, with a higher incidence of thrombocytopenia, the most frequent toxic effect, than that observed with control therapy

Late relapse after hematopoietic stem cell transplantation for acute leukemia: a retrospective study by SFGM-TC.

peer reviewedLate relapse (LR) after allogeneic hematopoietic stem cell transplantation (AHSCT) for acute leukemia is a rare event (nearly 4.5%) and raises the questions of prognosis and outcome after salvage therapy. We performed a retrospective multicentric study between January 1, 2010, and December 31, 2016, using data from the French national retrospective register ProMISe provided by the SFGM-TC (French Society for Bone Marrow Transplantation and Cellular Therapy). We included patients presenting with LR, defined as a relapse occurring at least 2 years after AHSCT. We used the Cox model to identify prognosis factors associated with LR. During the study period, a total of 7582 AHSCTs were performed in 29 centers, and 33.8% of patients relapsed. Among them, 319 (12.4%) were considered to have LR, representing an incidence of 4.2% for the entire cohort. The full dataset was available for 290 patients, including 250 (86.2%) with acute myeloid leukemia and 40 (13.8%) with acute lymphoid leukemia. The median interval from AHSCT to LR was 38.2 months (interquartile range [IQR], 29.2 to 49.7 months), and 27.2% of the patients had extramedullary involvement at LR (17.2% exclusively and 10% associated with medullary involvement). One-third of the patients had persistent full donor chimerism at LR. Median overall survival (OS) after LR was 19.9 months (IQR, 5.6 to 46.4 months). The most common salvage therapy was induction regimen (55.5%), with complete remission (CR) obtained in 50.7% of cases. Ninety-four patients (38.5%) underwent a second AHSCT, with a median OS of 20.4 months (IQR, 7.1 to 49.1 months). Nonrelapse mortality after second AHSCT was 18.2%. The Cox model identified the following factors as associated with delay of LR: disease status not in first CR at first HSCT (odds ratio [OR], 1.31; 95% confidence interval [CI], 1.04 to 1.64; P = .02) and the use of post-transplantation cyclophosphamide (OR, 2.23; 95% CI, 1.21 to 4.14; P = .01). Chronic GVHD appeared to be a protective factor (OR, .64; 95% CI, .42 to .96; P = .04). The prognosis of LR is better than in early relapse, with a median OS after LR of 19.9 months. Salvage therapy associated with a second AHSCT improves outcome and is feasible, without creating excess toxicity