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Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling
Journal article   Open access   Peer reviewed

Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling

Viktor H Ahlqvist, Paul Madley-Dowd, Amanda Ly, Jessica Rast, Michael Lundberg, Egill Jónsson-Bachmann, Daniel Berglind, Dheeraj Rai, Cecilia Magnusson and Brian K Lee
Pharmacoepidemiology and drug safety, 5614
15 Mar 2023
DOI: https://doi.org/10.1002/pds.5614
PMID: 36919941
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1002/pds.5614View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Bias amplification confounding causal inference pharmacoepidemiology
Estimating causal effects in observational pharmacoepidemiology is a challenging task, as it is often plagued by confounding by indication. Restricting the sample to those with an indication for drug use is a commonly performed procedure; indication-based sampling ensures that the exposed and unexposed are exchangeable on the indication - limiting the potential for confounding by indication. However, indication-based sampling has received little scrutiny, despite the hazards of exposure-related covariate control. Using simulations of varying levels of confounding and applied examples we describe bias amplification under indication-based sampling. We demonstrate that indication-based sampling in the presence of unobserved confounding can give rise to bias amplification, a self-inflicted phenomenon where one inflates pre-existing bias through inappropriate covariate control. Additionally, we show that indication-based sampling generally leads to a greater net bias than alternative approaches, such as regression adjustment. Finally, we expand on how bias amplification should be reasoned about when distinct clinically relevant effects on the outcome among those with an indication exist (effect-heterogeneity). We conclude that studies using indication-based sampling should have robust justification - and that it should by no means be considered unbiased to adopt such approaches. As such, we suggest that future observational studies stay wary of bias amplification when considering drug indications. This article is protected by copyright.

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Collaboration types
Domestic collaboration
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Web of Science research areas
Pharmacology & Pharmacy
Public, Environmental & Occupational Health
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