Sibling Comparison Designs: Addressing Confounding Bias with Inclusion of Measured Confounders

Genetically informative research designs are becoming increasingly popular as a way to strengthen causal inference with their ability to control for genetic and shared environmental confounding. Co-twin control (CTC) models, a special case of these designs using twin samples, decompose the overall effect of exposure on outcome into a within- and between-twin-pair term. Ideally, the within-twin-pair term would serve as an estimate of the exposure effect controlling for genetic and shared environmental factors, but it is often confounded by factors not shared within a twin-pair. Previous simulation work has shown that if twins are less similar on an unmeasured confounder than they are on an exposure, the within-twin-pair estimate will be a biased estimate of the exposure effect, even more biased than the individual, unpaired estimate. The current study uses simulation and analytical derivations to show that while incorporating a covariate related to the nonshared confounder in CTC models always reduces bias in the within-pair estimate, it will be less biased than the individual estimate only in a narrow set of circumstances. The best case for bias reduction in the within-pair estimate occurs when the within-twin-pair correlation in exposure is less than the correlation in the confounder and the twin-pair correlation in the covariate is high. Additionally, the form of covariate inclusion is compared between adjustment for only one's own covariate value and adjustment for the deviation of one's own value from the covariate twin-pair mean. Results show that adjusting for the deviation from the twin-pair mean results in equal or reduced bias.