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UBC Theses and Dissertations

Enhancing the robustness of instrumental variable estimation with potentially invalid instruments and its application to Mendelian randomization Osea, Jana


In epidemiology and medicine, identifying the causal relationship between an exposure and an outcome is crucial to gain valuable insights of disease mechanisms and to improve patient care. However, a common problem when attempting to extract the causal relationship between an exposure and an outcome in observational studies is the presence of unmeasured confounding. To address this issue, instrumental variable (IV) estimation methods have been proposed to capture the relationship between the exposure and the outcome that is unaffected by the confounding variables. In particular, Mendelian Randomization (MR) is a statistical methodology that uses genetic variants as instruments. However, the validity of these genetic variants as instruments is often questionable due to the presence of pleiotropy and linkage disequilibrium. In practice, it is often difficult to ascertain the validity of the instruments as it requires complete knowledge of the involved genes' function. Furthermore, exposure and outcome data are often contaminated by outlying observations. In this thesis, we propose a novel two-step robust and penalized IV estimator and an algorithm to compute it, called the Robustified Some Valid Some Invalid Instrumental Variable Estimator (rsisVIVE), based on the sisVIVE method of Kang et al. (2016). The rsisVIVE estimates the causal effect of an exposure on an outcome using observational data in the presence of invalid instruments while tolerating large proportions of outlying observations. Simulation results show that the rsisVIVE more accurately estimates the causal parameter than the sisVIVE when instruments are weak and when there are no outlying observations. The rsisVIVE also outperforms competitor IV estimators in all cases when there are large proportions of outlying observations.

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