Handling Attrition in Intensive Longitudinal Mental Heath Studies: Challenges and Solutions

• Activity Number: 20
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Hui Xie*+
• Keywords: Missing data ; Intensive ; nonignorability

Abstract

Missing data are ubiquitous in mental health studies that utilize modern intensive measurement methods. For example, in data collected using Ecological Momentary Assessment (EMA) methods, there can be a moderate amount of missing data because of study participants' nonresponses to those random prompts. It is often suspected that missing data caused by such prompt nonresponses are nonrandom so that the observed data may be a selected nonrandom subset of a person's background mood. Such nonignorable missingness needs to be properly accounted for in data analyses. However, unlike in traditional studies, nonignorable missingness in these intensive data poses significant new analytic challenges and calls for more general, flexible and robust methods that are applicable in these studies to quantify and improve the reliability, validity and usability of the collected data. To meet these challenges, we develop principled and parsimonious statistical measures that are applicable to these new types of mental health data to quantify the reliability and validity of empirical findings to nonignorable missingness.