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Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling
Viktor H Ahlqvist1, Paul Madley-Dowd2,3, Amanda Ly2,3
1Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
Pharmacoepidemiology and Drug Safety
|March 15, 2023
Summary
Indication-based sampling in pharmacoepidemiology can amplify bias, especially with unobserved confounding. Researchers should carefully justify its use and consider alternative methods to avoid inflated bias in causal effect estimation.
Area of Science:
- Pharmacoepidemiology
- Observational studies
- Causal inference
Background:
- Estimating causal effects in observational pharmacoepidemiology is difficult due to confounding by indication.
- Indication-based sampling is a common method to address this, but its potential biases are not well understood.
Purpose of the Study:
- To scrutinize indication-based sampling and its impact on bias amplification in observational pharmacoepidemiology.
- To evaluate the performance of indication-based sampling compared to alternative methods like regression adjustment.
Main Methods:
- Simulations with varying levels of confounding.
- Applied examples from pharmacoepidemiology.
- Analysis of bias amplification under different confounding scenarios.
Main Results:
- Indication-based sampling can amplify bias, particularly with unobserved confounding.
- This method may result in greater net bias compared to regression adjustment.
- Bias amplification needs careful consideration when effect heterogeneity exists.
Conclusions:
- Indication-based sampling requires strong justification and is not inherently unbiased.
- Future observational studies should be cautious about bias amplification when using drug indications.
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