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

Author:

Ahlqvist Viktor H.1ORCID,Madley‐Dowd Paul23,Ly Amanda23,Rast Jessica4,Lundberg Michael1,Jónsson‐Bachmann Egill1,Berglind Daniel15,Rai Dheeraj267,Magnusson Cecilia15,Lee Brian K.148

Affiliation:

1. Department of Global Public Health Karolinska Institutet Stockholm Sweden

2. Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School University of Bristol Bristol UK

3. MRC Integrative Epidemiology Unit University of Bristol Bristol UK

4. A. J. Drexel Autism Institute Drexel University Philadelphia Pennsylvania USA

5. Centre for Epidemiology and Community Medicine Region Stockholm Stockholm Sweden

6. NIHR Biomedical Research Centre University of Bristol Bristol UK

7. Bristol Autism Spectrum Service Avon and Wiltshire Partnership NHS Mental Health Trust Bristol UK

8. Department of Epidemiology and Biostatistics Drexel University School of Public Health Philadelphia Pennsylvania USA

Abstract

AbstractPurposeEstimating 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.MethodsUsing simulations of varying levels of confounding and applied examples we describe bias amplification under indication‐based sampling.ResultsWe 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).ConclusionWe 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.

Funder

Erik och Edith Fernströms Stiftelse för Medicinsk Forskning

Karolinska Institutet

National Institutes of Health

Svenska Sällskapet för Medicinsk Forskning

Publisher

Wiley

Subject

Pharmacology (medical),Epidemiology

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