Benoît Liquet

Type-II generalized family-wise error rate formulas with application to sample size determination

Multiple endpoints are increasingly used in clinical trials. The significance of some of these clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. The usual approach in multiple hypothesis testing is to control the family-wise error rate, which is defined as the probability that at least one type-I error is made. More recently, the q-generalized family-wise error rate has been introduced to control the probability of making at least q false rejections. For procedures controlling this global type-I error rate, we define a type-II r-generalized family-wise error rate, which is directly related to the r-power defined as the probability of rejecting at least r false null hypotheses. We obtain very general power formulas that can be used to compute the sample size for single-step and step-wise procedures. These are implemented in our R package rPowerSampleSize available on the CRAN, making them directly available to end users. Complexities of the formulas are presented to gain insight into computation time issues. Comparison with Monte Carlo strategy is also presented. We compute sample sizes for two clinical trials involving multiple endpoints: one designed to investigate the effectiveness of a drug against acute heart failure and the other for the immunogenicity of a vaccine strategy against pneumococcus. Copyright

A multivariate approach to investigate the combined biological effects of multiple exposures

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.

Sparse partial least squares with group and subgroup structure: Sparse partial least squares with group and subgroup structure

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. Our R package sgspls is available at https://github.com/matt-sutton/sgspls.

Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses: Immune markers and risk of lymphoma and MM

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

Maintenance des sessions Pré-requis et objectif

Les commandes elementaires consistent soit en des expressions, soit en des affectations obtenues au moyen de la fleche . Si une expression est tapee, elle est evaluee, le resultat est affiche puis perdu. Une affectation evalue aussi une expression, mais n’affiche pas forcement le resultat. Ce resultat est alors stocke dans un objet.

Mathématiques de base: calcul matriciel, intéegration, optimisation

Le tableau suivant fournit une liste quasi exhaustive des fonctions mathematiques les plus classiques.

Initiation à la programmation en R

Le point fort du systeme R est qu’il integre un vrai langage de programmation. Nous verrons qu’il propose des concepts de programmation tres originaux. Le concept d’objet est tres present dans le langage R. La programmation orientee objet utilisee dans R est transparente pour l’utilisateur dans le sens o’ il n’a pas besoin d’en comprendre la theorie pour pouvoir l’utiliser. Il n’en est pas de meme lorsque l’on se place du point de vue du developpeur souhaitant respecter l’esprit du langage R.