Using Structural-Nested Models to Estimate the Effect of Cluster-Level Adherence on Individual-Level Outcomes with a Three-Armed Cluster-Randomized Trial

• Activity Number: 468
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Babette Brumback*+ and Zhulin He and Mansi Prasad and Matthew Freeman and Richard Rheingans
• Companies: University of Florida and and University of Florida and Emory University and University of Florida
• Keywords: unmeasured confounding ; instrumental variables ; structural-nested models ; complex survey data ; adherence ; randomized clinical trial

Abstract

Much attention has been paid to estimating the causal effect of adherence to a randomized protocol using instrumental variables to adjust for unmeasured confounding. Researchers tend to use the instrumental variable within one of the three main frameworks: regression with an endogenous variable, principal stratification, or structural-nested modeling.We found in our literature review that even in simple settings, causal interpretations of analyses with endogenous regressors can be ambiguous or rely on a strong assumption that can be difficult to interpret. Principal stratification and structural-nested modeling are alternative frameworks that render unambiguous causal interpretations based on assumptions that are, arguably, easier to interpret. Our interest stems from a wish to estimate the effect of cluster-level adherence on individual-level binary outcomes with a three-armed cluster-randomized trial and polytomous adherence. Principal stratification approaches to this problem are quite challenging because of the sheer number of principal strata involved. Therefore, we developed a structural-nested modeling approach and, in the process, extended the methodology to accommodate clu