Natalizumab treatment shows low cumulative probabilities of confirmed disability worsening to EDSS milestones in the long-term setting.

Abstract Background Though the Expanded Disability Status Scale (EDSS) is commonly used to assess disability level in relapsing-remitting multiple sclerosis (RRMS), the criteria defining disability progression are used for patients with a wide range of baseline levels of disability in relatively short-term trials. As a result, not all EDSS changes carry the same weight in terms of future disability, and treatment benefits such as decreased risk of reaching particular disability milestones may not be reliably captured. The objectives of this analysis are to assess the probability of confirmed disability worsening to specific EDSS milestones (i.e., EDSS scores ≥3.0, ≥4.0, or ≥6.0) at 288 weeks in the Tysabri Observational Program (TOP) and to examine the impact of relapses occurring during natalizumab therapy in TOP patients who had received natalizumab for ≥24 months. Methods TOP is an ongoing, open-label, observational, prospective study of patients with RRMS in clinical practice. Enrolled patients were naive to natalizumab at treatment initiation or had received ≤3 doses at the time of enrollment. Intravenous natalizumab (300 mg) infusions were given every 4 weeks, and the EDSS was assessed at baseline and every 24 weeks during treatment. Results Of the 4161 patients enrolled in TOP with follow-up of at least 24 months, 3253 patients with available baseline EDSS scores had continued natalizumab treatment and 908 had discontinued (5.4% due to a reported lack of efficacy and 16.4% for other reasons) at the 24-month time point. Those who discontinued due to lack of efficacy had higher baseline EDSS scores (median 4.5 vs. 3.5), higher on-treatment relapse rates (0.82 vs. 0.23), and higher cumulative probabilities of EDSS worsening (16% vs. 9%) at 24 months than those completing therapy. Among 24-month completers, after approximately 5.5 years of natalizumab treatment, the cumulative probabilities of confirmed EDSS worsening by 1.0 and 2.0 points were 18.5% and 7.9%, respectively (24-week confirmation), and 13.5% and 5.3%, respectively (48-week confirmation). The risks of 24- and 48-week confirmed EDSS worsening were significantly higher in patients with on-treatment relapses than in those without relapses. An analysis of time to specific EDSS milestones showed that the probabilities of 48-week confirmed transition from EDSS scores of 0.0–2.0 to ≥3.0, 2.0–3.0 to ≥4.0, and 4.0–5.0 to ≥6.0 at week 288 in TOP were 11.1%, 11.8%, and 9.5%, respectively, with lower probabilities observed among patients without on-treatment relapses (8.1%, 8.4%, and 5.7%, respectively). Conclusions In TOP patients with a median (range) baseline EDSS score of 3.5 (0.0–9.5) who completed 24 months of natalizumab treatment, the rate of 48-week confirmed disability worsening events was below 15%; after approximately 5.5 years of natalizumab treatment, 86.5% and 94.7% of patients did not have EDSS score increases of ≥1.0 or ≥2.0 points, respectively. The presence of relapses was associated with higher rates of overall disability worsening. These results were confirmed by assessing transition to EDSS milestones. Lower rates of overall 48-week confirmed EDSS worsening and of transitioning from EDSS score 4.0–5.0 to ≥6.0 in the absence of relapses suggest that relapses remain a significant driver of disability worsening and that on-treatment relapses in natalizumab-treated patients are of prognostic importance

Propagation d'épidémies et survie en grande dimension : des modèles statistiques classiques aux méthodes d'apprentissage

This Research Accreditation (HDR) in Mathematics consists of statistics applied to epidemiology and clinical research. Modern technologies make it possible to generate data on thousands of variables or observations, either via the internet that can be used to build a medical signal, or directly from genomic patient data, radiomics, medico-administrative databases, disease surveillance by smart medical devices, etc. This big data requires the development of specific statistical or machine learning methods in order to provide answers to the underlying clinical questions that are robust and reliable. My research work is mainly concerned with data on durations (time to event) and time series of epidemic spread. The particularities of these two types of data present complex challenges which also require simulation studies. The models presented, developed or applied are, among others, classical or survival random forests, support vector machines, autoregressive models, Cox models, SIR (Susceptible-Infectious-Removed) compartmental models, linear and nonlinear mixed models, or joined models.This work mainly concerns medical applications of major public health issues, like influenza, gastroenteritis, COVID19, glioblastoma, tumor growth, or variations of iron and hepcidin during the menstrual cycle.Cette Habilitation à Diriger des Recherches (HDR) de Mathématiques s'inscrit en statistique appliquée à l'épidémiologie et à la recherche clinique.Les technologies modernes permettent de générer des données sur des milliers de variables ou d'observations, que ce soit via internet qui peut être utilisé pour construire un signal médical, ou directement à partir de données-patients génomiques, radiomiques, des bases de données médico-administratives, desurveillance des maladies par des dispositifs médicaux intelligents, etc. Ces données massives nécessitent le développement de méthodes spécifiques statistiques ou d'apprentissage qui rendent robustes et fiables les réponses aux questions cliniques sous-jacentes. Mon travail de recherche s'intéresse principalement aux données de durées (d'apparition d'événements) et de séries temporelles de propagation d'épidémies.Les particularités de ces deux types de données engendrent des sujets méthodologiques qui nécessitent également des études de simulations.Les modèles présentés, développés ou appliqués sont, entre autres, des forêts aléatoires classiques ou de survie, des méthodes à noyaux adaptées, des modèles autorégressifs, des modèles de Cox, des modèles compartimentaux SIR (Susceptible-Infectious-Removed), des modèles mixtes linéaires et non linéaires, ou des modèles joints.Ce travail concerne essentiellement des applications médicales d'enjeu de santé publique majeur comme la grippe, la gastro-entérite, la COVID19, le glioblastome, la croissance tumorale, ou les variations de quantité de fer et d'hepcidine pendant le cycle menstruel

Modélisation et analyse de la co-circulation de virus grippaux (diffusion en population, variabilité génomique et impact clinique)

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect models

saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect model

Familiar faces recognition across the lifespan: Subjective memorability enhances encoding beyond prior knowledge

International audienc

Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm

Conditionally accepted for publication on 2017-07-13Estimated delay in publication - one year (editor's communication, 2018-04-05)International audienceThe saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation-maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community

Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event

International audienceRandom survival forests (RSF) have emerged as valuable tools in medical research. They have shown their utility in modelling complex relationships between predictors and survival outcomes, overcoming linearity or low dimensionality assumptions. Nevertheless, RSF have not been adapted to right-censored data with recurrent events (RE). This work introduces RecForest, an extension of RSF and tailored for RE data, leveraging principles from survival analysis and ensemble learning. RecForest adapts the splitting rule to account for RE, with or without a terminal event, by employing the pseudo-score test or the Wald test derived from the marginal Ghosh-Lin model. The ensemble estimate is constructed by aggregating the expected number of events from each tree. Performance metrics involve a concordance index (C-index) tailored for RE analysis, along with an extension of the mean squared error (MSE). A comprehensive evaluation was conducted on both simulated and open-source data. We compared RecForest against the non-parametric mean cumulative function and the Ghosh-Lin model. Across the simulations and application, RecForest consistently outperforms, exhibiting C-index values ranging from 0.64 to 0.80 and lowest MSE metrics. As analysing time-to-recurrence data is critical in medical research, the proposed method represents a valuable addition to the analytical toolbox in this domain

Meta-analysis of interstitial pneumonia in studies evaluating iodine-131-labeled lipiodol for hepatocellular carcinoma using exact likelihood approach.

International audiencePURPOSE: Iodine-131-labeled lipiodol is currently licensed for unresectable hepatocellular carcinoma with portal thrombosis. It is thought to be well tolerated. Cases of interstitial pneumonia have been reported, but their frequency (≈2%) has not been well estimated. Quantifying adverse drug event frequency requires an appropriate statistical approach because standard methods are biased. METHODS: To estimate the frequency of interstitial pneumonia in patients with hepatocellular carcinoma receiving iodine-131-labeled lipiodol, we conducted a systematic review of English articles using MEDLINE and EMBASE. All types of articles were considered except case reports. Primary outcome measure was symptomatic interstitial pneumonia based on investigators' judgment. All pooled analyses were based on a random effects meta-analysis model using an exact likelihood approach based on the binomial within-study distribution. RESULTS: Ten studies, including 142 patients, used low activity per dose, ranging from 0.3 to 1.1 GBq. No respiratory adverse event was noticed in these studies. Eighteen studies, including 542 patients, evaluated higher activity per dose, around 2.2 GBq; 24 cases of interstitial pneumonia were reported in these studies. Estimated frequency of interstitial pneumonia was 1.6% (95%CI, 0.4-6.4%) after one high dose and 4.1% (95%CI, 1.0-16.0%) after two or more high doses. CONCLUSIONS: The frequency of interstitial pneumonia appears higher and more precise than previously estimated. The risk appears to be related to the number of injections and the dose level per injection. Generalized linear mixed models using the exact binomial within-study distribution initially described to summarize data on diagnostic evaluation could be relevant for drug-related adverse reaction frequency assessment