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.
Disciplines
Clinical Trials | Epidemiology | Longitudinal Data Analysis and Time Series | Statistical Models
Suggested Citation
Henneman, Tanya A.; van der Laan, Mark Johannes; and Hubbard, Alan E., "Estimating Causal Parameters in Marginal Structural Models with Unmeasured Confounders Using Instrumental Variables" (January 2002). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 104.
https://biostats.bepress.com/ucbbiostat/paper104
Included in
Clinical Trials Commons, Epidemiology Commons, Longitudinal Data Analysis and Time Series Commons, Statistical Models Commons