10 citations · 55 across the 21 of their papers we have counts for
18 papers · 1 filter
ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models
Matteo Amestoy, Mark A. van de Wiel, Wessel N. van Wieringen
ProfileGLMM is an R package integrating Generalised Linear Mixed Models (GLMMs) as the outcome model for Bayesian profile regression. This statistical framework simultaneously i) e…
Bivariate deconvolution for cancer detection after surgery
Nuria Senar, Stavros Makrodimitris, Michel H. Hof +3
Detection of minimal residual disease (MRD) in cancer patients after surgery can provide an early marker for disease recurrence and guide subsequent treatment decisions. Accurate a…
Fusion of Tree-induced Regressions for Clinico-genomic Data
Jeroen M. Goedhart, Mark A. van de Wiel, Wessel N. van Wieringen +1
Cancer prognosis is often based on a set of omics covariates and a set of established clinical covariates such as age and tumor stage. Combining these two sets poses challenges. Fi…
A Flexible Model for Record Linkage
Kayané Robach, Stéphanie L van der Pas, Mark A van de Wiel +1
Combining data from various sources empowers researchers to explore innovative questions, for example those raised by conducting healthcare monitoring studies. However, the lack of…
Bayesian regression discontinuity design with unknown cutoff
Julia Kowalska, Mark van de Wiel, Stéphanie van der Pas
The regression discontinuity design (RDD) is a quasi-experimental approach used to estimate the causal effects of an intervention assigned based on a cutoff criterion. RDD exploits…
A framework for interpretation and testing of sparse canonical correlations
Nuria Senar, Mark van de Wiel, Aeilko Zwinderman +1
In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Cor…
Linked shrinkage to improve estimation of interaction effects in regression models
Mark A. van de Wiel, Matteo Amestoy, Jeroen Hoogland
We address a classical problem in statistics: adding two-way interaction terms to a regression model. As the covariate dimension increases quadratically, we develop an estimator th…
Informative co-data learning for high-dimensional Horseshoe regression
Claudio Busatto, Mark van de Wiel
High-dimensional data often arise from clinical genomics research to infer relevant predictors of a particular trait. A way to improve the predictive performance is to include info…
Think before you shrink: Alternatives to default shrinkage methods can improve prediction accuracy, calibration and coverage
Mark A. van de Wiel, Gwenaël G. R. Leday, Jeroen Hoogland +3
While shrinkage is essential in high-dimensional settings, its use for low-dimensional regression-based prediction has been debated. It reduces variance, often leading to improved…
Penalised regression with multiple sources of prior effects
Armin Rauschenberger, Zied Landoulsi, Mark A. van de Wiel +1
In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we cons…
Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves
Jeroen M. Goedhart, Thomas Klausch, Mark A. van de Wiel
In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is pr…
A Bayesian accelerated failure time model for interval censored three-state screening outcomes
Thomas Klausch, Eddymurphy U. Akwiwu, Mark A. van de Wiel +2
Women infected by the Human papilloma virus are at an increased risk to develop cervical intraepithalial neoplasia lesions (CIN). CIN are classified into three grades of increasing…
Semi-supervised empirical Bayes group-regularized factor regression
Magnus M. Münch, Mark A. van de Wiel, Aad W. van der Vaart +1
The features in high dimensional biomedical prediction problems are often well described with lower dimensional manifolds. An example is genes that are organised in smaller functio…
Adaptive group-regularized logistic elastic net regression
Magnus M. Münch, Carel F. W. Peeters, Aad W. van der Vaart +1
In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (a) p-values from a previ…
Learning from a lot: Empirical Bayes in high-dimensional prediction settings
Mark A. van de Wiel, Dennis E. te Beest, Magnus Münch
Empirical Bayes is a versatile approach to `learn from a lot' in two ways: first, from a large number of variables and second, from a potentially large amount of prior information,…
An empirical Bayes approach to network recovery using external knowledge
Gino B. Kpogbezan, Aad W. van der Vaart, Wessel N. van Wieringen +2
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such…
Gene network reconstruction using global-local shrinkage priors
Gwenaël G. R. Leday, Mathisca C. M. de Gunst, Gino B. Kpogbezan +3
Reconstructing a gene network from high-throughput molecular data is often a challenging task, as the number of parameters to estimate easily is much larger than the sample size. A…
Better prediction by use of co-data: Adaptive group-regularized ridge regression
Mark A. van de Wiel, Tonje G. Lien, Wina Verlaat +2
For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous pr…