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The impact of confounder selection in propensity scores when applied to prospective cohort studies in pregnancy.

Authors
  • Xu, Ronghui1
  • Hou, Jue2
  • Chambers, Christina D3
  • 1 Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, United States; Department of Mathematics, University of California, San Diego, La Jolla, CA, USA. Electronic address: [email protected] , (United States)
  • 2 Department of Mathematics, University of California, San Diego, La Jolla, CA, USA.
  • 3 Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, United States; Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States. , (United States)
Type
Published Article
Journal
Reproductive toxicology (Elmsford, N.Y.)
Publication Date
Jun 01, 2018
Volume
78
Pages
75–80
Identifiers
DOI: 10.1016/j.reprotox.2018.04.003
PMID: 29635047
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Our work was motivated by small cohort studies on the risk of birth defects in infants born to pregnant women exposed to medications. We controlled for confounding using propensity scores (PS). The extremely rare events setting renders the matching or stratification infeasible. In addition, the PS itself may be formed via different approaches to select confounders from a relatively long list of potential confounders. We carried out simulation experiments to compare different combinations of approaches: IPW or regression adjustment, with 1) including all potential confounders without selection, 2) selection based on univariate association between the candidate variable and the outcome, 3) selection based on change in effects (CIE). The simulation showed that IPW without selection leads to extremely large variances in the estimated odds ratio, which help to explain the empirical data analysis results that we had observed. Copyright © 2018 Elsevier Inc. All rights reserved.

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