Contemporary longitudinal methods for the study of trauma and posttraumatic stress disorder

Traditional methods for analyzing trends in longitudinal data have typically emphasized average group change over time. In this article, we propose multilevel, regression-based methods for examining inter-individual differences in intra-individual change and apply these methods to research in trauma and posttraumatic stress disorder (PTSD). The outcome or dependent variable of interest is reconceptualized as an index of dynamic change reflecting the trend or trajectory of an individual's PTSD symptom severity scores across time. A basic statistical model is presented, and analyses and findings are demonstrated with an existing database used in previously published studies. The methods offer promise for future study of the natural course of PTSD chronicity or recovery, risk and resilience factors that influence individual growth or decline, and critical timepoints for intervention.