Causal Mediation Analysis in Multi-Level Intervention and Multi-Component Mediator Case

• Activity Number: 233
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Cheng Zheng*+ and Xiao-Hua (Andrew) Zhou
• Companies: University of Washington and University of Washington
• Keywords: Causal Inference ; Generalized Estimating Equation ; Correlated Data ; Missing Data

Abstract

Mediation analysis is an important issue in social and behavirol sciences as it helps to understand why a behavioral intervention works. To yield a causal interpretation the most common approach (e.g., Baron and Kenny, 1986), as discussed by (Imai, Keele, and Tingley 2010), need an often-unrealistic assumption of "sequential ignorability". Rank preserving model (RPM; Ten Have et al., 2007) is proposed to relax this assumption. However, RPM is restricted to the case with binary intervention and single mediator. Also, it needs the strong "rank preserve" assumption. We propose a new model that can handle multi-level intervention and multi-component mediator with weaker assumption. Also, our model has the ability to handle correlated data and missing data. And our method can be used in many different research questions, such as treatment compliance.