Co-Twin Control Models: Assessing Bias from Measured and Unmeasured 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... [ view full abstract ]

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 an exposure on an 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 a non-shared 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 estimate¹. The current study uses simulation and analytical derivations to show that incorporating a covariate related to the non-shared confounder in CTC models can reduce this bias in many cases. 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 both ways of covariate adjustment demonstrate comparable results in terms of bias when the covariate is a weak measure of the non-shared confounder. When the covariate is a stronger measure of the non-shared confounder, adjusting only for one’s own covariate value can reduce bias more than including the twin pair mean in all cases except when the twin pair correlation in the non-shared confounder is zero.

¹Frisell, T., Öberg, S., Kuja-Halkola, R., & Sjölander, A. (2012). Sibling comparison designs: Bias from non-shared confounders and measurement error. Epidemiology, 5, 713–720.