Data from: Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data

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  • 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 introductory statistics courses and is more difficult than correlation to estimate from data. In this article, we motivate the use of MI in the analyses of epidemiologic data, while providing a general introduction to estimation and interpretation. We illustrate its utility through a retrospective study relating intraoperative heart rate (HR) and mean arterial pressure (MAP). We: (i) show postoperative mortality is associated with decreased MI between HR and MAP and (ii) improve existing postoperative mortality risk assessment by including MI and additional hemodynamic statistics. ... [Read More]

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22 files (817 MB)
Data Citation
  • Young, A., van den Boom, W., Schroeder, R., Krishnamoorthy, V., Raghunathan, K., Wu, H., Dunson, D. (2022). Data from: Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data. Duke Research Data Repository. https://doi.org/10.7924/r45q52g2t
DOI
  • 10.7924/r45q52g2t
Publication Date
ARK
  • ark:/87924/r45q52g2t
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Collection Dates
  • 1/1/1999 - 12/31/2003
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Related Materials
  • Young, A. L., van den Boom, W., Schroeder, R. A., Krishnamoorthy, V., Raghunathan, K., Wu, H., & Dunson, D. B. (2023). Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data. PloS One, 18(4), e0284904-e0284904.  https://doi.org/10.1371/journal.pone.0284904
Funding Agency
  • National Science Foundation
  • National Institute of Environmental Sciences
Grant Number
  • Award # 1045153
  • R01-ESO17240
Title
  • Data from: Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data
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file details ReadMe_Mutual Information.txt 2022-05-17 Public Download
file details 2907 Data File 1.xlsx 2022-05-17 Public Download
file details 2907 Data File 2.xlsx 2022-05-17 Public Download
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file details 2907 Data File 9.xlsx 2022-05-17 Public Download

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