Exact and efficient solutions of the LMC Multitask Gaussian Process model

The Linear Model of Co-regionalization (LMC) is a very general model of multitask gaussian process for regression or classification. While its expressivity and conceptual simplicity are appealing, naive implementations have cubic complexity in the number of datapoints and number of tasks, making approximations mandatory for most applications. However, recent work has shown that under some conditions the latent processes of the model can be decoupled, leading to a complexity that is only linear in the number of said processes. We here extend these results, showing from the most general assumptions that the only condition necessary to an efficient exact computation of the LMC is a mild hypothesis on the noise model. We introduce a full parametrization of the resulting \emph{projected LMC} model, and an expression of the marginal likelihood enabling efficient optimization. We perform a parametric study on synthetic data to show the excellent performance of our approach, compared to an unrestricted exact LMC and approximations of the latter. Overall, the projected LMC appears as a credible and simpler alternative to state-of-the art models, which greatly facilitates some computations such as leave-one-out cross-validation and fantasization.Comment: 21 pages, 5 figures, submitted to AISTAT

Predicting long-term disability outcomes in patients with MS treated with teriflunomide in TEMSO

To predict long-term disability outcomes in TEMSO core (NCT00134563) and extension (NCT00803049) studies in patients with relapsing forms of MS treated with teriflunomide. Methods: A post hoc analysis was conducted in a subgroup of patients who received teriflunomide in the core study, had MRI and clinical relapse assessments at months 12 (n = 552) and 18, and entered the extension. Patients were allocated risk scores for disability worsening (DW) after 1 year of teriflunomide treatment: 0 = low risk; 1 = intermediate risk; and 2-3 = high risk, based on the occurrence of relapses (0 to 652) and/or active (new and enlarging) T 2 -weighted (T 2 w) lesions ( 643 or >3) after the 1-year MRI. Patients in the intermediate-risk group were reclassified as responders or nonresponders (low or high risk) according to relapses and T 2 w lesions on the 18-month MRI. Long-term risk (7 years) of DW was assessed by Kaplan-Meier survival curves. Results: In patients with a score of 2-3, the risk of 12-week-confirmed DW over 7 years was significantly higher vs those with a score of 0 (hazard ratio [HR] = 1.96, p = 0.0044). Patients reclassified as high risk at month 18 (18.6%) had a significantly higher risk of DW vs those in the low-risk group (81.4%; HR = 1.92; p = 0.0004). Conclusions: Over 80% of patients receiving teriflunomide were classified as low risk (responders) and had a significantly lower risk of DW than those at increased risk (nonresponders) over 7 years of follow-up in TEMSO. Close monitoring of relapses and active T 2 w lesions after short-term teriflunomide treatment predicts a differential rate of subsequent DW long term. ClinicalTrials.gov identifier: TEMSO, NCT00134563; TEMSO extension, NCT0080304

Predicting long-term disability outcomes in patients with MS treated with teriflunomide in TEMSO

To predict long-term disability outcomes in TEMSO core (NCT00134563) and extension (NCT00803049) studies in patients with relapsing forms of MS treated with teriflunomide. Methods: A post hoc analysis was conducted in a subgroup of patients who received teriflunomide in the core study, had MRI and clinical relapse assessments at months 12 (n = 552) and 18, and entered the extension. Patients were allocated risk scores for disability worsening (DW) after 1 year of teriflunomide treatment: 0 = low risk; 1 = intermediate risk; and 2-3 = high risk, based on the occurrence of relapses (0 to â¥2) and/or active (new and enlarging) T 2 -weighted (T 2 w) lesions (â¤3 or >3) after the 1-year MRI. Patients in the intermediate-risk group were reclassified as responders or nonresponders (low or high risk) according to relapses and T 2 w lesions on the 18-month MRI. Long-term risk (7 years) of DW was assessed by Kaplan-Meier survival curves. Results: In patients with a score of 2-3, the risk of 12-week-confirmed DW over 7 years was significantly higher vs those with a score of 0 (hazard ratio [HR] = 1.96, p = 0.0044). Patients reclassified as high risk at month 18 (18.6%) had a significantly higher risk of DW vs those in the low-risk group (81.4%; HR = 1.92; p = 0.0004). Conclusions: Over 80% of patients receiving teriflunomide were classified as low risk (responders) and had a significantly lower risk of DW than those at increased risk (nonresponders) over 7 years of follow-up in TEMSO. Close monitoring of relapses and active T 2 w lesions after short-term teriflunomide treatment predicts a differential rate of subsequent DW long term. ClinicalTrials.gov identifier: TEMSO, NCT00134563; TEMSO extension, NCT00803049

Magnetic resonance imaging outcomes from a phase III trial of teriflunomide

Objective: The purpose of this study was to determine the effects of oral teriflunomide on multiple sclerosis (MS) pathology inferred by magnetic resonance imaging (MRI). Methods: Patients ( n=1088) with relapsing MS were randomized to once-daily teriflunomide 7 mg or 14 mg, or placebo, for 108 weeks. MRI was recorded at baseline, 24, 48, 72 and 108 weeks. Annualized relapse rate and confirmed progression of disability (sustained ≥12 weeks) were the primary and key secondary outcomes. The principal MRI outcome was change in total lesion volume. Results: After 108 weeks, increase in total lesion volume was 67.4% ( p=0.0003) and 39.4% ( p=0.0317) lower in the 14 and 7 mg dose groups versus placebo. Other measures favoring teriflunomide were accumulated enhanced lesions, combined unique activity, T2-hyperintense and T1-hypointense component lesion volumes, white matter volume, and a composite MRI score; all were significant for teriflunomide 14 mg and most significant for 7 mg versus placebo. Conclusions: Teriflunomide provided benefits on brain MRI activity across multiple measures, with a dose effect evident on several markers. These effects were also consistent across selected subgroups of the study population. These findings complement clinical data showing significant teriflunomide-related reductions in relapse rate and disease progression, and demonstrate containment of MRI-defined disease progression

Long-term safety and efficacy of teriflunomide : nine-year follow-up of the randomized TEMSO study

This study was funded by Sanofi Genzyme.OBJECTIVE: To report safety and efficacy outcomes from up to 9 years of treatment with teriflunomide in an extension (NCT00803049) of the pivotal phase 3 Teriflunomide Multiple Sclerosis Oral (TEMSO) trial (NCT00134563). METHODS: A total of 742 patients entered the extension. Teriflunomide-treated patients continued the original dose; those previously receiving placebo were randomized 1:1 to teriflunomide 14 mg or 7 mg. RESULTS: By June 2013, median (maximum) teriflunomide exposure exceeded 190 (325) weeks per patient; 468 patients (63%) remained on treatment. Teriflunomide was well-tolerated with continued exposure. The most common adverse events (AEs) matched those in the core study. In extension year 1, first AEs of transient liver enzyme increases or reversible hair thinning were generally attributable to patients switching from placebo to teriflunomide. Approximately 11% of patients discontinued treatment owing to AEs. Twenty percent of patients experienced serious AEs. There were 3 deaths unrelated to teriflunomide. Soon after the extension started, annualized relapse rates and gadolinium-enhancing T1 lesion counts fell in patients switching from placebo to teriflunomide, remaining low thereafter. Disability remained stable in all treatment groups (median Expanded Disability Status Scale score ≤2.5; probability of 12-week disability progression ≤0.48). CONCLUSIONS: In the TEMSO extension, safety observations were consistent with the core trial, with no new or unexpected AEs in patients receiving teriflunomide for up to 9 years. Disease activity decreased in patients switching from placebo and remained low in patients continuing on teriflunomide. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that long-term treatment with teriflunomide is well-tolerated and efficacy of teriflunomide is maintained long-term.Publisher PDFPeer reviewe

Pertinence des critères d'évaluation utilisés dans les essais cliniques sur la maladie d'Alzheimer

Le travail de la table ronde (TR) n°1 "Pertinence des critères d'évaluation dans les essais cliniques sur la maladie d'Alzheimer" a utilisé comme fil conducteur le guideline publié en juillet 2008 par l'EMEA (European Medicines Agency) pour le développement des médicaments dans la maladie d'Alzheimer et autres démences, et a porté principalement sur deux des trois indications thérapeutiques recensées dans le guideline – les traitements symptomatiques et les médicaments modifiant le cours évolutif de la maladie – à l'exclusion de l'approche de prévention primaire. La discussion s'est focalisée sur deux aspects principaux : l'amélioration de la sélection des patients dans les essais cliniques et les critères de jugements cliniques et les biomarqueurs. Les suggestions suivantes ont été formulées : Renforcer l'intérêt pour les études cliniques dans les phases précoces de la maladie d'Alzheimer (y compris prodromales), notamment pour les candidats médicaments modifiant le cours évolutif de la maladie. Renforcer l'expertise des centres de recherche avec les biomarqueurs, de façon à faciliter leur utilisation ultérieure dans les études cliniques, soit pour compléter la description des patients inclus, soit à titre de critères de sélection. De plus, les travaux en cours en France de standardisation inter-centre de neuro-imagerie et des dosages de substrats du liquide céphalo-rachidien sont essentiels pour préparer leur utilisation multicentrique. Favoriser la réalisation d'études ancillaires de biomarqueurs, greffées sur des études cliniques. Renforcer la formation et l'expérience des cotateurs avec les échelles fonctionnelles, qui doivent maintenant constituer une des deux évaluations principales des études pivotales, et avec les items additionnels de l'ADAS-cog (Alzheimer's Disease Assessment Scale, Cognitive sub scale) qui sont utiles pour les formes précoces de maladie d'Alzheimer. Améliorer la connaissance des propriétés des échelles cliniques fonctionnelles, par l'analyse détaillée des bases de données disponibles d'études cliniques, dans des collaborations public/privé. Améliorer la connaissance des correspondances entre échelles utilisées dans les études cliniques et outils utilisés dans la pratique quotidienne (généralement différents), de façon à étayer l'interprétation de la pertinence clinique des résultats d'études cliniques. Réfléchir aux éventuels besoins d'adaptation des outils d'évaluation clinique aux évolutions sociétales, notamment pour les évaluations des performances cognitives. Discuter la possibilité de mesures de protections supplémentaires des données pour les médicaments candidats anti-Alzheimer médicaments modifiant le cours évolutif de la maladie étant donné l'impact négatif pour tous les acteurs des contraintes de durée et difficultés des essais thérapeutiques. Rediscuter l'éthique et l'acceptabilité potentielle d'études contre placebo en monothérapie sur des durées de 6-9 mois

Relevance of the Evaluation Criteria Used in Clinical Trials for Alzheimer's Disease

The roundtable 1 “Relevance of the evaluation criteria used in clinical trials for Alzheimer's disease” made reference to the guideline published by the EMEA (European Medicines Agency) in July 2008 on the development of new treatments for Alzheimer's disease (AD) and other dementias, and addressed principally two of the three indications listed in the guideline: symptomatic improvement and disease-modification (primary prevention was hardly discussed). The discussions focussed on two main aspects: improvement of the selection of patients in clinical trials and clinical evaluation and biomarkers. The following suggestions were made:
Reinforce the interest for clinical trials at the early stages of AD (including prodromal stage), particularly for disease-modifiers. Strengthen the research centers' expertise with biomarkers, in a perspective of subsequent use in clinical trials, either for the description of the patients included, or as part of selection criteria. Furthermore, ongoing intercenter validation studies, in France, of neuro-imaging and biomarker assays in CSF, are essential for preparing multicenter clinical trials. Facilitate the conduct of ancillary studies with biomarkers, grafted on clinical studies. Further develop the training and experience of raters with functional scales, which are now required as one of the two primary endpoints in pivotal clinical trials, and with the additional items of ADAS-cog (Alzheimer's Disease Assessment scale, Cognitive sub scale), which are useful for the early stages of AD. Improve knowledge of functional clinical scales by in depth analysis of available databases, through public/private collaborations. Improve knowledge of relationship between rating scales used in clinical trials and tools used in clinical practice (which are usually different), in order to provide supporting evidence for the interpretation of the clinical relevance of clinical trials results. Consider the potential needs of adaptation of rating scales to the societal changes, in particular for the evaluation of cognitive performance. Discuss the possibility of measures for extending data protection for candidate disease-modifiers, considering the negative impact for all players of the constraints of duration and the difficulties of clinical trials. Further discuss the ethics and acceptability of placebo-controlled monotherapy studies on durations of 6-9 months

An EIM-based compression-extrapolation tool for efficient treatment of homogenized cross-section data

International audienceNuclear reactor simulators implementing the widespread two-steps deterministic calculation scheme tend to produce a large volume of intermediate data at the interface of their two subcodes – up to dozens or even hundred of gigabytes – which can be so cumbersome that it hinders the global performance of the code. The vast majority of this data consists of ‘‘few-groups homogenized cross-sections’’, nuclear quantities stored in the form of tabulated multivariate functions which can be precomputed to a large extent.It has been noticed in Tomatis (2021) that few-groups homogenized cross-sections are highly redundant— that is, they exhibit strong correlations, which paves the way for the use of compression techniques. Wehere pursue this line of work by introducing a new coupled compression/surrogate modeling tool based on the Empirical Interpolation Method, an algorithm originally developed in the framework of partial differential equations (Barrault et al., 2004). This EIM-compression method is based on the infinite norm ∥ ⋅ ∥∞, and proceeds in a greedy manner by iteratively trying to approximate the data and incorporating the chunks of information which cause the largest error. In the process, it generates a vector basis and a set of interpolation points, which provide an elementary surrogate model that can be used to approximate future data from little information. The algorithm is also very suitable for parallelization and out-of-core computation (processing of data too large for the computer RAM) and very easy to apprehend and implement. This method enables us to both efficiently compress cross-sections and spare a large fraction of the required lattice calculations. We investigate its performance on large realistic nuclear data replicating the notorious VERA benchmark (Godfrey, 2014) (20 energy groups, pin-by-pin homogenization, 10 particularized isotopes). Compression loss, memory savings and speed are analyzed both from a data-centric point of view in the perspective of applications in neutronics, and by comparison with an existing and widely-used method – stochastic truncated SVD – to assess mathematical efficiency. We discuss the usage of our surrogate model and its sensitivity to the choice of the training set. The method is shown to be competitive in terms of accuracy and speed, provide important memory savings and spare a large amount of physics code computation; all this could facilitate the adoption of fine-grain modelization schemes (pin-by-pin and many-groups homogenization, particularization of many isotopes) in industrial setups. A Github repository is available,1 which contains all the methods used for the article

Données IRM à long terme des patients sous tériflunomide : résultats de l’extension de l’étude de phase II

National audienc