Causal Inference with Longitudinal Outcomes and Nonignorable Drop-Out

• Activity Number: 186
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Maria Josefsson*+ and Xavier Luna de Luna and Lars Luna Nyberg
• Companies: Umeå University and Umeå University and Umeå University
• Keywords: Causal Inference ; Missing data ; Non-ignorable drop-out ; Longitudinal data

Abstract

In many evaluation studies of a causal agent (treatment), analysts use observational data in which the treatment and control conditions are not randomly assigned to participants. By using propensity score matching the analysts can balance observed covariates between treatment and control groups and hence reduce potential bias in estimated causal effects. Incomplete data is common in longitudinal studies due, e.g., to participants' death or withdrawal. Such drop-out is said to be non-ignorable when it depends on the participant's underlying rate of change in the outcome. Not taking into account non-ignorable drop-out may yield biased estimates of causal effects. In this paper we propose a method for estimating the average causal effect of a treatment, at baseline, on the trajectories of some longitudinal outcome under the presence of non-ignorable drop-out. We illustrate this method with an analysis of the causal effect of living alone on memory performance based on data from a large longitudinal study conducted in Umeå, Sweden.