Maarten Neerincx

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Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting,(More)
This document provides Supplementary Material for the paper " ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs ". To study the inferential performance of ShrinkBayes when the null-hypotheses are of the equality-type, H 0i : β i = 0, we compared 4 types of spike-containing priors under 20 different(More)
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