Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study.
Air pollution
Confounder
Indirect adjustment
Simulation
Survival analysis
Journal
Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
30
06
2021
revised:
26
08
2021
accepted:
30
08
2021
pubmed:
7
9
2021
medline:
8
1
2022
entrez:
6
9
2021
Statut:
ppublish
Résumé
An indirect adjustment method was developed to control for unmeasured confounders in a large administrative cohort study. A previous study that proposed the indirect adjustment method assessed the validity of the method by simulations but did not consider the direction of bias and scenarios with multiple missing confounders. In this study, we evaluated the direction and the magnitude of bias of the indirect adjustment method with multiple correlated unmeasured confounders using simulation and empirical datasets. A simulation study was conducted to compare the bias of the indirect adjustment by varying the number of confounders, magnitude of correlation between confounders, and the number of adjustment variables. An empirical study was conducted by applying the indirect adjustment method to the association between PM The simulations of the present study demonstrated that 1) when a confounder is positively associated with both exposure and outcome, indirect adjustment might bias the effect size downward; 2) the magnitude of bias might depend on the correlation between unmeasured confounders; and 3) indirect adjustment for multiple missing confounders at once could result in a higher bias than that for some of the missing confounders. Empirical analyses also showed consistent results, but the bias of indirectly adjusted effect estimates was sometimes larger than that of unadjusted effect estimates. The indirect adjustment method is a promising technique to reduce the bias from unmeasured confounding; however, it should be implemented carefully, particularly when there are multiple correlated unmeasured confounders of the same direction.
Sections du résumé
BACKGROUND
An indirect adjustment method was developed to control for unmeasured confounders in a large administrative cohort study. A previous study that proposed the indirect adjustment method assessed the validity of the method by simulations but did not consider the direction of bias and scenarios with multiple missing confounders. In this study, we evaluated the direction and the magnitude of bias of the indirect adjustment method with multiple correlated unmeasured confounders using simulation and empirical datasets.
METHODS
A simulation study was conducted to compare the bias of the indirect adjustment by varying the number of confounders, magnitude of correlation between confounders, and the number of adjustment variables. An empirical study was conducted by applying the indirect adjustment method to the association between PM
RESULTS
The simulations of the present study demonstrated that 1) when a confounder is positively associated with both exposure and outcome, indirect adjustment might bias the effect size downward; 2) the magnitude of bias might depend on the correlation between unmeasured confounders; and 3) indirect adjustment for multiple missing confounders at once could result in a higher bias than that for some of the missing confounders. Empirical analyses also showed consistent results, but the bias of indirectly adjusted effect estimates was sometimes larger than that of unadjusted effect estimates.
CONCLUSIONS
The indirect adjustment method is a promising technique to reduce the bias from unmeasured confounding; however, it should be implemented carefully, particularly when there are multiple correlated unmeasured confounders of the same direction.
Identifiants
pubmed: 34487697
pii: S0013-9351(21)01287-1
doi: 10.1016/j.envres.2021.111992
pii:
doi:
Substances chimiques
Particulate Matter
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
111992Informations de copyright
Copyright © 2021 Elsevier Inc. All rights reserved.