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

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 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.

SUBMITTER: Saunders GRB

PROVIDER: S-EPMC7170177 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Saunders Gretchen R B GRB McGue Matt M Malone Stephen M SM

Twin research and human genetics : the official journal of the International Society for Twin Studies 20190927 5

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 environm ...[more]

PMID: 31559947

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Project description:BackgroundControlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method.MethodsWe are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders.ResultsThe simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99-2.93) mmol/L with per 1- kg/m2 higher BMI and DBP increased 0.37 (95% CI: 0.03-0.76) mmol/L with per 1- kg/m2 higher BMI. Moreover, 1- kg/m2 increase in BMI was causally associated with a 1.61 (95% CI: 0.96-2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91-55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09-4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value - 0.20 (95% CI: - 1.71-1.44). And, the effect value of FBG per 1- kg/m2 higher BMI was 0.56 (95% CI: - 0.24-2.18).ConclusionsWe propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access.

Dataset Information

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

Publications

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

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