Case-control studies are designed to study associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate population effects in such studies. IPW estimators are robust, as they only require correct specification of the mean regression model of the secondary outcome on covariates and knowledge of the disease prevalence. However, IPW estimators are inefficient relative to estimators that make additional assumptions about the data generating mechanism. We propose a class of estimators for the effect of risk factors on a secondary outcome in case-control studies that combine IPW with an additional modeling assumption: specification of the disease outcome probability model. We incorporate this model via a mean zero control function. We derive the class of all regular and asymptotically linear estimators corresponding to our modeling assumption when the secondary outcome mean is modeled using either the identity or the log link. We find the efficient estimator in our class of estimators and show that it reduces to standard IPW when the model for the primary disease outcome is unrestricted, and is more efficient than standard IPW when the model is either parametric or semiparametric.
- Case-control study
- Genetic association studies
- Inverse probability weighting
- Semiparametric inference
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty