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Estimating marginal causal effects in a secondary analysis of case-control data
Emma Persson1, Ingeborg Waernbaum1, Torbjörn Lind2
1Department of Statistics, USBE, Umeå University, SE-90187, Umeå, Sweden.
Statistics in Medicine
|March 10, 2017
Summary
This study introduces a method for analyzing case-control data to estimate treatment effects. It adjusts for sampling bias in secondary analyses, showing improved causal effect estimation.
Area of Science:
- Epidemiology
- Biostatistics
- Causal Inference
Background:
- Case-control studies are common in epidemiology.
- Secondary analysis of case-control data can evaluate outcomes related to the case-defining event.
- Estimating marginal or average causal effects requires accounting for the sampling design.
Purpose of the Study:
- To develop and evaluate methods for estimating average causal effects in secondary analyses of case-control data.
- To address bias introduced by ignoring the sampling scheme in case-control studies.
- To propose a design-weighted matching estimator for average causal effects.
Main Methods:
- Secondary analysis of matched and unmatched case-control data.
- Development of estimators for average treatment effects.
- Demonstration of a design-weighted matching estimator.
- Bias analysis for estimators ignoring the sampling scheme.
Main Results:
- Identified components of bias when the sampling scheme is ignored.
- Demonstrated a design-weighted matching estimator for average causal effects.
- Evaluated finite sample properties of the estimator through simulations.
- Applied the method to study childhood onset diabetes and adult antidepressant use.
Conclusions:
- A design-weighted matching estimator can provide unbiased estimates of average causal effects in secondary analyses of case-control data.
- Adjusting for the sampling scheme is crucial for accurate estimation of marginal effects.
- The proposed methods are applicable to real-world epidemiological research, as shown in the diabetes study.
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