Mendelian Randomization Analysis for Dichotomous Disease Traits Under Outcome-Dependent Sampling

• Activity Number: 149
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): James Dai*+
• Companies: Fred Hutchinson Cancer Research Center
• Keywords: Mendelian randomization ; outcome-dependent sampling ; instrumental variable ; genetic variants ; inverse probability weighted estimator

Abstract

Mendelian randomization studies often have dichotomous disease outcomes, multiple genetic instrumental variables, and outcome-dependent sampling for genotypes. These features pose challenges to classical instrumental variable estimation methods. In this talk causal estimands in structural models and in potential outcomes models will be discussed. We define a class of minimal distance estimators for the causal effect in structural modeling and derive optimal estimator in this class. We also consider efficient estimation procedures for structural mean models. Finally, we discuss various inverse probability weighted methods to account for outcome-dependent sampling. We present simulations and data applications to illusrate the validity and the efficiency of our proposed methods.