Propensity Process for Time-Varying Covariates with Application to Assessing Treatment Effect in ALS Patients

• Activity Number: 173
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Pallavi S. Mishra-Kalyani*+ and Qi Long and Brent A. Johnson
• Companies: Emory University and Emory University and Emory University
• Keywords: Causal Inference ; Matching ; Propensity Scores ; Observational Data ; Time-varying Covariates ; Longitudinal Data

Abstract

When a non-randomized treatment can be given at anytime during follow-up, complex issues of selection bias and confounding are of paramount concern. Although some propensity score methods, specifically the Generalized Propensity Score of Hirano and Imbens (2004) and Propensity Function of Imai van Dyk (2004), that are able to account for non-binary treatment assignments and time-varying covariates, these methods are not able to establish balance for treatment assignment conditional on an entire covariate process. This balancing property is particularly important when time-varying covariates are measured at uneven or inconsistent time intervals that do not coincide with the time of treatment. In this paper we propose the Propensity Process as a method that is able to address these complex features that are common to observational registries with longitudinally measured data. We demonstrate the Propensity Process and compare it to standard non-binary propensity score methods in an application to data from the Emory ALS Clinic Registry.