Mendelian Randomization Analyses Under Case-control Sampling | 17922
Epidemiology: Open Access
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endelian randomization studies use inherited genetic variants as instrumental variables to infer the causal effect of an
intermediate phenotype on a disease outcome. For relative rare dichotomous disease outcomes, case-control sampling
is efficient and commonly used to ascertain genetic variants. In this article, we assess the impact of case-control sampling on
Mendelian randomization analyses with a dichotomous disease outcome and a continuous intermediate phenotype, and we
focus on the two-stage least squares (2SLS) estimation. We show that the 2SLS procedure, though merely an approximation
in this setting, provides a valid test and a generally conservative estimate of the causal effect. Under case-control sampling,
the first stage of the 2SLS procedure becomes estimation of secondary trait association. Through theoretical development and
simulations, we compare the na?ve estimator, the inverse probability weighted (IPW) estimator and the maximum likelihood
(ML) estimator for the secondary trait association, and more importantly, the resulting 2SLS estimates of the causal effect.
We also include in our comparison the causal odds ratio estimate derived from structural mean models (SMM), a consistent
estimator that are estimated via generalized methods of moments (GMM). Our results suggest that the naive estimator is
substantially biased under the alternative, yet unbiased under the null hypothesis of no causal effect; for small to moderate
sample size, the ML estimator yields smaller variance and mean squared error than both the IPW estimator and the GMM
estimator; the GMM estimator delivers the smallest bias, but generally larger variance, and sometimes it has issues in algorithm
stability and convergence.
James Y Dai has graduated from Department of Biostatistics, University of Washington, Seattle, in 2007. He is associate member in Fred Hutchinson Cancer
Research Center and affiliate Associate Professor in Department of Biostatistics, University of Washington. He has been actively publishing in methodological
research on genome-wide genetic association, gene-environment interactions, and adaptive algorithms. As part of his research interest, he has been working in
causal mediation analysis, Mendelian randomization, and estimation direct and indirect effects in genetic epidemiology.
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