Between-Subject and Within-Subject Model Mixtures for Classifying HIV Treatment Response

We present a method for using longitudinal data  to classify individuals into clinically-relevant population subgroups. This is achieved by treating ``subgroup'' as a categorical covariate whose value is unknown for each individual, and predicting its value using mixtures of models that represent ``typical'' longitudinal data from each subgroup.  Under a nonlinear mixed effects model framework, two types of model mixtures are presented, both of which have their advantages. Following illustrative simulations, longitudinal viral load data for HIV-positive patients is used to predict whether they are responding -- completely, partially or not at all -- to a new drug treatment

An improved SAEM algorithm for maximum likelihood estimation in mixtures of non linear mixed effects models

International audienceWe propose a new methodology for maximum likelihood estimation in mixtures of non linear mixed effects models (NLMEM). Such mixtures of models include mixtures of distributions, mixtures of structural models and mixtures of residual error models. Since the individual parameters inside the NLMEM are not observed, we propose to combine the EM algorithm usually used for mixtures models when the mixture structure concerns an observed variable, with the Stochastic Approximation EM (SAEM) algorithm, which is known to be suitable for maximum likelihood estimation in NLMEM and also has nice theoretical properties. The main advantage of this hybrid procedure is to avoid a simulation step of unknown group labels required by a †full†version of SAEM. The resulting MSAEM (Mixture SAEM) algorithm is now implemented in the Monolix software. Several criteria for classification of subjects and estimation of individual parameters are also proposed. Numerical experiments on simulated data show that MSAEM performs well in a general framework of mixtures of NLMEM. Indeed, MSAEM provides an estimator close to the maximum likelihood estimator in very few iterations and is robust with regard to initialization. An application to pharmacokinetic (PK) data demonstrates the potential of the method for practical applications

Strongly self-interacting vector dark matter via freeze-in

We study a vector dark matter (VDM) model in which the dark sector couples to the Standard Model sector via a Higgs portal. If the portal coupling is small enough the VDM can be produced via the freeze-in mechanism. It turns out that the electroweak phase transition have a substantial impact on the prediction of the VDM relic density. We further assume that the dark Higgs boson which gives the VDM mass is so light that it can induce strong VDM self-interactions and solve the small-scale structure problems of the Universe. As illustrated by the latest LUX data, the extreme smallness of the Higgs portal coupling required by the freeze-in mechanism implies that the dark matter direct detection bounds are easily satisfied. However, the model is well constrained by the indirect detections of VDM from BBN, CMB, AMS-02, and diffuse $\gamma$/X-rays. Consequently, only when the dark Higgs boson mass is at most of ${\cal O}({\rm keV})$ does there exist a parameter region which leads to a right amount of VDM relic abundance and an appropriate VDM self-scattering while satisfying all other constraints simultaneously.Comment: 29 pages, 8 figure

Occurrence of Anti-Drug Antibodies against Interferon-Beta and Natalizumab in Multiple Sclerosis: A Collaborative Cohort Analysis

Immunogenicity of biopharmaceutical products in multiple sclerosis is a frequent side effect which has a multifactorial etiology. Here we study associations between anti-drug antibody (ADA) occurrence and demographic and clinical factors. Retrospective data from routine ADA test laboratories in Sweden, Denmark, Austria and Germany (Dusseldorf group) and from one research study in Germany (Munich group) were gathered to build a collaborative multi-cohort dataset within the framework of the ABIRISK project. A subset of 5638 interferon-beta (IFN beta)-treated and 3440 natalizumab-treated patients having data on at least the first two years of treatment were eligible for interval-censored time-to-event analysis. In multivariate Cox regression, IFN beta-1a subcutaneous and IFN beta-1b subcutaneous treated patients were at higher risk of ADA occurrence compared to IFN beta-1a intramuscular-treated patients (pooled HR = 6.4, 95% CI 4.9-8.4 and pooled HR = 8.7, 95% CI 6.6-11.4 respectively). Patients older than 50 years at start of IFN beta therapy developed ADA more frequently than adult patients younger than 30 (pooled HR = 1.8, 95% CI 1.4-2.3). Men developed ADA more frequently than women (pooled HR = 1.3, 95% CI 1.1-1.6). Interestingly we observed that in Sweden and Germany, patients who started IFN beta in April were at higher risk of developing ADA (HR = 1.6, 95% CI 1.1-2.4 and HR = 2.4, 95% CI 1.5-3.9 respectively). This result is not confirmed in the other cohorts and warrants further investigations. Concerning natalizumab, patients older than 45 years had a higher ADA rate (pooled HR = 1.4, 95% CI 1.0-1.8) and women developed ADA more frequently than men (pooled HR = 1.4, 95% CI 1.0-2.0). We confirmed previously reported differences in immunogenicity of the different types of IFN beta. Differences in ADA occurrence by sex and age are reported here for the first time. These findings should be further investigated taking into account other exposures and biomarkers

Occurrence of Anti-Drug Antibodies against Interferon-Beta and Natalizumab in Multiple Sclerosis: A Collaborative Cohort Analysis

Immunogenicity of biopharmaceutical products in multiple sclerosis is a frequent side effect which has a multifactorial etiology. Here we study associations between anti-drug antibody (ADA) occurrence and demographic and clinical factors. Retrospective data from routine ADA test laboratories in Sweden, Denmark, Austria and Germany (Dusseldorf group) and from one research study in Germany (Munich group) were gathered to build a collaborative multi-cohort dataset within the framework of the ABIRISK project. A subset of 5638 interferon-beta (IFN beta)-treated and 3440 natalizumab-treated patients having data on at least the first two years of treatment were eligible for interval-censored time-to-event analysis. In multivariate Cox regression, IFN beta-1a subcutaneous and IFN beta-1b subcutaneous treated patients were at higher risk of ADA occurrence compared to IFN beta-1a intramuscular-treated patients (pooled HR = 6.4, 95% CI 4.9-8.4 and pooled HR = 8.7, 95% CI 6.6-11.4 respectively). Patients older than 50 years at start of IFN beta therapy developed ADA more frequently than adult patients younger than 30 (pooled HR = 1.8, 95% CI 1.4-2.3). Men developed ADA more frequently than women (pooled HR = 1.3, 95% CI 1.1-1.6). Interestingly we observed that in Sweden and Germany, patients who started IFN beta in April were at higher risk of developing ADA (HR = 1.6, 95% CI 1.1-2.4 and HR = 2.4, 95% CI 1.5-3.9 respectively). This result is not confirmed in the other cohorts and warrants further investigations. Concerning natalizumab, patients older than 45 years had a higher ADA rate (pooled HR = 1.4, 95% CI 1.0-1.8) and women developed ADA more frequently than men (pooled HR = 1.4, 95% CI 1.0-2.0). We confirmed previously reported differences in immunogenicity of the different types of IFN beta. Differences in ADA occurrence by sex and age are reported here for the first time. These findings should be further investigated taking into account other exposures and biomarkers

Clinical practice of analysis of anti-drug antibodies against interferon beta and natalizumab in multiple sclerosis patients in Europe:A descriptive study of test results

Antibodies against biopharmaceuticals (anti-drug antibodies, ADA) have been a well-integrated part of the clinical care of multiple sclerosis (MS) in several European countries. ADA data generated in Europe during the more than 10 years of ADA monitoring in MS patients treated with interferon beta (IFNβ) and natalizumab have been pooled and characterized through collaboration within a European consortium. The aim of this study was to report on the clinical practice of ADA testing in Europe, considering the number of ADA tests performed and type of ADA assays used, and to determine the frequency of ADA testing against the different drug preparations in different countries. A common database platform (tranSMART) for querying, analyzing and storing retrospective data of MS cohorts was set up to harmonize the data and compare results of ADA tests between different countries. Retrospective data from six countries (Sweden, Austria, Spain, Switzerland, Germany and Denmark) on 20,695 patients and on 42,555 samples were loaded into tranSMART including data points of age, gender, treatment, samples, and ADA results. The previously observed immunogenic difference among the four IFNβ preparations was confirmed in this large dataset. Decreased usage of the more immunogenic preparations IFNβ-1a subcutaneous (s.c.) and IFNβ-1b s.c. in favor of the least immunogenic preparation IFNβ-1a intramuscular (i.m.) was observed. The median time from treatment start to first ADA test correlated with time to first positive test. Shorter times were observed for IFNβ-1b-Extavia s.c. (0.99 and 0.94 years) and natalizumab (0.25 and 0.23 years), which were introduced on the market when ADA testing was already available, as compared to IFNβ-1a i.m. (1.41 and 2.27 years), IFNβ-1b-Betaferon s.c. (2.51 and 1.96 years) and IFNβ-1a s.c. (2.11 and 2.09 years) which were available years before routine testing began. A higher rate of anti-IFNβ ADA was observed in test samples taken from older patients. Testing for ADA varies between different European countries and is highly dependent on the policy within each country. For drugs where routine monitoring of ADA is not in place, there is a risk that some patients remain on treatment for several years despite ADA positivity. For drugs where a strategy of ADA testing is introduced with the release of the drug, there is a reduced risk of having ADA positive patients and thus of less efficient treatment. This indicates that potential savings in health cost might be achieved by routine analysis of ADA

Inference in non-linear mixed effects joints and mixtures models

Cette thèse est consacrée au développement de nouvelles méthodologies pour l'analyse des modèles non-linéaires à effets mixtes, à leur implémentation dans un logiciel accessible et leur application à des problèmes réels. Nous considérons particulièrement des extensions des modèles non-linéaires à effets mixtes aux modèles de mélange et aux modèles conjoints. Dans la première partie, nous proposons, dans le but d'avoir une meilleure maîtrise de l'hétérogénéité liée aux données sur des patients issus de plusieurs clusters, des extensions des MNLEM aux modèles de mélange. Nous proposons ensuite de combiner l'algorithme EM, utilisé traditionnellement pour les modèles de mélanges lorsque les variables étudiées sont observées, et l'algorithme SAEM, utilisé pour l'estimation de paramètres par maximum de vraisemblance lorsque ces variables ne sont pas observées. La procédure résultante, dénommée MSAEM, permet ainsi d'éviter l'introduction d'une étape de simulation des covariables catégorielles latentes dans l'algorithme d'estimation. Cet algorithme est extrêmement rapide, très peu sensible à l'initialisation des paramètres, converge vers un maximum (local) de la vraisemblance et est implémenté dans le logiciel Monolix.La seconde partie de cette Thèse traite de la modélisation conjointe de l'évolution d'un marqueur biologique au cours du temps et les délais entre les apparitions successives censurées d'un évènement d'intérêt. Nous considérons entre autres, les censures à droite, les multiples censures par intervalle d'évènements répétés. Les paramètres du modèle conjoint résultant sont estimés en maximisant la vraisemblance jointe exacte par un algorithme de type MCMC-SAEM. Cette méthodologie est désormais disponible sous MonolixThe main goal of this thesis is to develop new methodologies for the analysis of non linear mixed-effects models, along with their implementation in accessible software and their application to real problems. We consider particularly extensions of non-linear mixed effects model to mixture models and joint models. The study of these two extensions is the essence of the work done in this document, which can be divided into two major parts. In the first part, we propose, in order to have a better control of heterogeneity linked to data of patient issued from several clusters, extensions of NLMEM to mixture models. We suggest in this Thesis to combine the EM algorithm, traditionally used for mixtures models when the variables studied are observed, and the SAEM algorithm, used to estimate the maximum likelihood parameters when these variables are not observed. The resulting procedure, referred MSAEM, allows avoiding the introduction of a simulation step of the latent categorical covariates in the estimation algorithm. This algorithm appears to be extremely fast, very little sensitive to parameters initialization and converges to a (local) maximum of the likelihood. This methodology is now available under the Monolix software. The second part of this thesis deals with the joint modeling of the evolution of a biomarker over time and the time between successive appearances of a possibly censored event of interest. We consider among other, the right censoring and interval censorship of multiple events. The parameters of the resulting joint model are estimated by maximizing the exact joint likelihood by using a MCMC-SAEM algorithm. The proposed methodology is now available under Monolix

Inférence dans les modèles conjoints et de mélange non-linéaires à effets mixtes

The main goal of this thesis is to develop new methodologies for the analysis of non linear mixed-effects models, along with their implementation in accessible software and their application to real problems. We consider particularly extensions of non-linear mixed effects model to mixture models and joint models. The study of these two extensions is the essence of the work done in this document, which can be divided into two major parts. In the first part, we propose, in order to have a better control of heterogeneity linked to data of patient issued from several clusters, extensions of NLMEM to mixture models. We suggest in this Thesis to combine the EM algorithm, traditionally used for mixtures models when the variables studied are observed, and the SAEM algorithm, used to estimate the maximum likelihood parameters when these variables are not observed. The resulting procedure, referred MSAEM, allows avoiding the introduction of a simulation step of the latent categorical covariates in the estimation algorithm. This algorithm appears to be extremely fast, very little sensitive to parameters initialization and converges to a (local) maximum of the likelihood. This methodology is now available under the Monolix software. The second part of this thesis deals with the joint modeling of the evolution of a biomarker over time and the time between successive appearances of a possibly censored event of interest. We consider among other, the right censoring and interval censorship of multiple events. The parameters of the resulting joint model are estimated by maximizing the exact joint likelihood by using a MCMC-SAEM algorithm. The proposed methodology is now available under Monolix.Cette thèse est consacrée au développement de nouvelles méthodologies pour l'analyse des modèles non-linéaires à effets mixtes, à leur implémentation dans un logiciel accessible et leur application à des problèmes réels. Nous considérons particulièrement des extensions des modèles non-linéaires à effets mixtes aux modèles de mélange et aux modèles conjoints. Dans la première partie, nous proposons, dans le but d'avoir une meilleure maîtrise de l'hétérogénéité liée aux données sur des patients issus de plusieurs clusters, des extensions des MNLEM aux modèles de mélange. Nous proposons ensuite de combiner l'algorithme EM, utilisé traditionnellement pour les modèles de mélanges lorsque les variables étudiées sont observées, et l'algorithme SAEM, utilisé pour l'estimation de paramètres par maximum de vraisemblance lorsque ces variables ne sont pas observées. La procédure résultante, dénommée MSAEM, permet ainsi d'éviter l'introduction d'une étape de simulation des covariables catégorielles latentes dans l'algorithme d'estimation. Cet algorithme est extrêmement rapide, très peu sensible à l'initialisation des paramètres, converge vers un maximum (local) de la vraisemblance et est implémenté dans le logiciel Monolix.La seconde partie de cette Thèse traite de la modélisation conjointe de l'évolution d'un marqueur biologique au cours du temps et les délais entre les apparitions successives censurées d'un évènement d'intérêt. Nous considérons entre autres, les censures à droite, les multiples censures par intervalle d'évènements répétés. Les paramètres du modèle conjoint résultant sont estimés en maximisant la vraisemblance jointe exacte par un algorithme de type MCMC-SAEM. Cette méthodologie est désormais disponible sous Monoli

Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients

International audienceAbstractBackgroundFor clinical genomic studies with high-dimensional datasets, tree-based ensemble methods offer a powerful solution for variable selection and prediction taking into account the complex interrelationships between explanatory variables. One of the key component of the tree-building process is the splitting criterion. For survival data, the classical splitting criterion is the Logrank statistic. However, the presence of a fraction of nonsusceptible patients in the studied population advocates for considering a criterion tailored to this peculiar situation.ResultsWe propose a bagging survival tree procedure for variable selection and prediction where the survival tree-building process relies on a splitting criterion that explicitly focuses on time-to-event survival distribution among susceptible patients.A simulation study shows that our method achieves good performance for the variable selection and prediction. Different criteria for evaluating the importance of the explanatory variables and the prediction performance are reported. Our procedure is illustrated on a genomic dataset with gene expression measurements from early breast cancer patients.ConclusionsIn the presence of nonsusceptible patients among the studied population, our procedure represents an efficient way to select event-related explanatory covariates with potential higher-order interaction and identify homogeneous groups of susceptible patients