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Updated: Mar 6, 2026
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
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Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering
Matthew E Levine1, David J Albers1, George Hripcsak1
1Department of Biomedical Informatics, Columbia University, New York, New York, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 9, 2017
Summary
Multivariate lagged regression models effectively identify adverse drug effects from electronic health records. These models offer higher accuracy and interpretability than simpler methods, improving clinical data analysis.
Area of Science:
- Biomedical Informatics
- Clinical Data Analysis
- Pharmacovigilance
Background:
- Electronic health records (EHR) contain valuable clinical and biological data.
- Time series analysis can uncover associations within EHR data.
- Identifying adverse drug effects (ADEs) is crucial for patient safety.
Purpose of the Study:
- To develop reliable, high-throughput methods for ADE identification.
- To compare univariate and multivariate lagged regression models for ADE detection.
- To enhance the interpretability and robustness of findings from EHR data.
Main Methods:
- Utilized univariate and multivariate lagged regression models.
- Investigated associations between 20 drug-laboratory measurement pairs.
- Incorporated autoregressive terms and inpatient admission indicators.
Main Results:
- Multivariate models showed higher sensitivity and specificity than univariate models.
- Autoregressive terms improved signal robustness for known associations.
- Inclusion of admission terms helped identify and attenuate confounding factors.
Conclusions:
- Multivariate lagged regression is a sensitive and specific method for ADE detection in EHRs.
- Model adjustments enhance the reliability of identifying drug-laboratory associations.
- This approach offers a straightforward way to analyze EHR data and control for confounding variables.
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