Estimating Causal Parameters in Marginal Structural Models with Unmeasured Confounders Using Instrumental Variables

Tanya A. Henneman, University of California, Berkeley
Mark Johannes van der Laan, University of California, Berkeley
Alan E. Hubbard, University of California, Berkeley

Abstract

For statisticians analyzing medical data, a significant problem in determining the causal effect of a treatment on a particular outcome of interest, is how to control for unmeasured confounders. Techniques using instrumental variables (IV) have been developed to estimate causal parameters in the presence of unmeasured confounders. In this paper we apply IV methods to both linear and non-linear marginal structural models. We study a specific class of generalized estimating equations that is appropriate to these data, and compare the performance of the resulting estimator to the standard IV method, a two-stage least squares procedure. Our results are applied to simulation studies and a data analysis example comparing treatment procedures for ruptured cerebral aneurysms.