Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data.

ABSTRACT: Given a large clinical database of longitudinal patient information including many covariates, it is computationally prohibitive to consider all types of interdependence between patient variables of interest. This challenge motivates the use of mutual information (MI), a statistical summary of data interdependence with appealing properties that make it a suitable alternative or addition to correlation for identifying relationships in data. MI: (i) captures all types of dependence, both linear and nonlinear, (ii) is zero only when random variables are independent, (iii) serves as a measure of relationship strength (similar to but more general than R2), and (iv) is interpreted the same way for numerical and categorical data. Unfortunately, MI typically receives little to no attention in intr

SUBMITTER: Young AL 

PROVIDER: S-EPMC10132663 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature