Repository logo
Log in(current)
Repository logoMIT Open ScholarshipDSpace@MIT
  1. Home
  2. MIT Open Access Articles
  3. MIT Open Access Articles
  4. Systematically exploring repurposing effects of antihypertensives

Systematically exploring repurposing effects of antihypertensives

Thumbnail Image
Download
Name

Pharmacoepidemiology and Drug - 2022 - Shahn - Systematically exploring repurposing effects of antihypertensives.pdf

Description
Published version
Size

947.61 KB

Format

Adobe PDF

Checksum (MD5)

48fe8329065bd64c4f3b673a22d58979

sword-2022-10-06T14:00:54.original.xml (130 B)
Original SWORD entry document
Author(s)
Shahn, Zach
•
Spear, Phoebe
•
Lu, Helen
•
Jiang, Sharon
•
Zhang, Suki
•
Deshmukh, Neil
•
Xu, Shenbo
•
Ng, Kenney
•
Welsch, Roy
•
Finkelstein, Stan
Date Issued
September 2022
Journal
Pharmacoepidemiology and Drug Safety
Publisher
Wiley
Citation
Shahn, Zach, Spear, Phoebe, Lu, Helen, Jiang, Sharon, Zhang, Suki et al. 2022. "Systematically exploring repurposing effects of antihypertensives." Pharmacoepidemiology and Drug Safety, 31 (9).
Version
Final published version
Abstract
With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Operations Research Center
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/145705
DOI of Published Version
https://doi.org/10.1002/pds.5491
Repository logo
PrivacyPermissionsAccessibilityContact us
Repository logo
Notify us about copyright concerns.