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There is ample epidemiologic evidence for an association of chronic hepatitis C virus (HCV) infection with B-cell non-Hodgkin lymphoma (B-NHL). B-NHL subtypes most frequently associated with HCV are marginal zone lymphoma and diffuse large B-cell lymphoma. The most convincing evidence for a causal relationship between HCV infection and lymphoma development is the observation of B-NHL regression after HCV eradication by antiviral therapy (AVT). In fact, for indolent HCV-associated B-NHL, first-line AVT instead of standard immune-chemotherapy might be considered. Molecular mechanisms of HCV-NHL development are still poorly understood. Three general theories have emerged to understand the HCV-induced lymphomagenesis: (1) continuous external stimulation of lymphocyte receptors by viral antigens and consecutive proliferation; (2) HCV replication in B cells with oncogenic effect mediated by intracellular viral proteins; (3) permanent B-cell damage, e.g., mutation of tumor suppressor genes, caused by a transiently intracellular virus (“hit and run” theory). This review systematically summarizes the data on epidemiology, interventional studies, and molecular mechanisms of HCV-associated B-NHL.
Motivation: Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. The present work aims at (i) developing a non-parametric alternative to Cohen’s d, which (ii) circumvents some of its numerical limitations and (iii) involves obvious changes in the data that do not affect the group means and are therefore not captured by Cohen’s d.
Results: We propose "Impact” as a novel non-parametric measure of effect size obtained as the sum of two separate components and includes (i) a difference-based effect size measure implemented as the change in the central tendency of the group-specific data normalized to pooled variability and (ii) a data distribution shape-based effect size measure implemented as the difference in probability density of the group-specific data. Results obtained on artificial and empirical data showed that “Impact”is superior to Cohen's d by its additional second component in detecting clearly visible effects not reflected in central tendencies. The proposed effect size measure is invariant to the scaling of the data, reflects changes in the central tendency in cases where differences in the shape of probability distributions between subgroups are negligible, but captures changes in probability distributions as effects and is numerically stable even if the variances of the data set or its subgroups disappear.
Conclusions: The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from big and heterogeneous data.