Application of robust regression in translational neuroscience studies with non-Gaussian outcome data.

ABSTRACT: Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer's disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approa

SUBMITTER: Malek-Ahmadi M 

PROVIDER: S-EPMC10847267 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature