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Confounders and intermediaries in case-control study designs: a strategy for distinguishing between the two when
Andrea Gruneir1, Connie Marras, Hadas Fischer
1Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada. andrea.gruneir@wchospital.ca
Pharmacoepidemiology and Drug Safety
|February 26, 2011
Summary
Distinguishing intermediaries from confounders is crucial in observational studies. A new exposure classification strategy improved bias estimation in antipsychotic studies, differentiating hospital use before and after drug initiation.
Area of Science:
- Pharmacoepidemiology
- Observational Study Design
- Biostatistics
Background:
- Intermediaries and confounders are distinct in exposure-outcome pathways.
- Case-control studies face challenges distinguishing them when measured by the same variable.
- Antipsychotic initiation and older adult mortality are influenced by hospital use, acting as both confounder and intermediary.
Purpose of the Study:
- To illustrate bias from conflating intermediaries and confounders.
- To propose a modified exposure classification strategy.
- To mitigate bias in observational studies using real-world data.
Main Methods:
- Utilized a case-control study of 5391 cases and 25,937 controls.
- Performed three analyses: full adjustment, reduced adjustment, and extended exposure classification.
- The extended strategy incorporated hospital use prior to antipsychotic initiation.
Main Results:
- Unadjusted odds ratio (OR) was 2.8.
- Full and reduced adjustments yielded ORs of 0.8 and 1.4, respectively.
- Extended classification showed an OR of 1.4 for those without prior hospital use.
Conclusions:
- Full analytic adjustment led to biased effect estimates.
- The extended strategy successfully differentiated hospital use as a confounder (prior) versus intermediary (subsequent).
- This novel approach may overcome limitations of traditional analytic adjustment alone.
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