Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework.

Background: The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables.

Methods: We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework.

Von Willebrand factor parameters as potential biomarkers for disease activity and coronary artery lesion in patients with Kawasaki disease.

Elevated von Willebrand factor (vWF):Antigen plasma levels have been observed in conjunction with cardiovascular diseases or vasculitis. The association of Kawasaki disease, a vascular inflammatory disease and vWF:Antigen, vWF:Collagen binding activity, and vWF multimers is unknown. We therefore investigated vWF parameters in 28 patients with acute Kawasaki disease in association with disease activity and coronary artery lesions.

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection.

Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question.

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.

Background: The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g.