Invited Speakers

IWSM 2020: Invited Speakers


  • Maria Durban (Professor of Statistics. University Carlos III of Madrid, Spain).
  • Montserrat Fuentes (Provost, Professor of Statistics, Actuarial Science and Biostatistics. University of Iowa, USA).
    Spatial Statistical Modelling of Neuroimaging Data
    Imaging data with thousands of spatially-correlated data points are common in many fields. In Neurosciences, magnetic resonance imaging (MRI) is a primary modality for studying brain structure and activity. Modeling spatial dependence of MRI data at different scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (millions of voxels) presents modeling challenges and serious computational constraints. Methods that account for spatial correlation often require very cumbersome matrix evaluations which are prohibitive for data of this size, and thus current methods typically reduce dimensionality by modeling covariance among regions of interest – coarser or larger spatial units – rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect important activation patterns in the brain and hence produce misleading results. To overcome these problems, we introduce a novel Bayesian Tensor approach, treating the brain image as response and having a vector of predictors. Our method provides estimates of the parameters of interest using a generalized sparsity principle. This method is implemented using a fully Bayesian approach to characterize different sources of uncertainty. We demonstrate posterior consistency and develop a computational efficient algorithm. The effectiveness of our approach is illustrated through simulation studies and the analysis of the effects of drug addiction on the brain structure. We implement this method to identify the effects of demographic information and cocaine addiction on the functioning of the brain.
  • Yudi Pawitan (Professor of Biostatistics. Karolinska Institutet, Sweden).
  • Virginie Rondeau (Directeur de Recherche, INSERM Bordeaux, France).
    The use of joint modelling to validate surrogate failure-time endpoints
    In many Biomedical areas, the identification and validation of surrogate endpoints is of prime interest to reduce the duration and/or size of clinical trials. Numerous validation methods have been proposed, the most popular is based on a two-step analysis strategy in the context of meta-analysis. For two failure time endpoints, two association measurements are usually considered, one at the individual level and one at the trial level. However, thus approach is not always available mainly due to convergence or estimation problems in clinical trials. We are presenting here different approaches based on joint frailty models and a one-step validation method with new attractive and well developed tools for the validation of failure time surrogate endpoints. Both individual- and trial-level surrogacy were evaluated using a new definition of Kendall's tau and the coefficient of determination.
    We aim in this work to popularize these new surrogate endpoints validation approaches by making the methods available in a user-friendly R package. Thus, we provide in the frailtypack R package numerous tools, including more flexible functions, for the validation of candidate surrogate endpoints, using data from multiple randomized clinical trials. We have especially the surrogate threshold effect which is used in combination with R^2_{trial} to make a decision concerning the validity of the surrogate endpoints. It is also possible thanks to frailtypack to predict the treatment effect on the true endpoint in a new trial using the treatment effect observed on the surrogate endpoint. The leave-one-out cross-validation is available for the assessment of the accuracy of the prediction using the joint surrogate model. Other tools concerned data generation, studies simulation and graphic representations. We illustrate the use of the new functions with both real data and simulated data.
  • Stijn Vansteelandt (Professor of Statistics. Ghent University, Belgium).