Combining group-based trajectory modeling and propensity score matching for causal inferences in nonexperimental longitudinal data.

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This article describes and applies a method for using observational longitudinal data to make more transparent causal inferences about the impact of such events on developmental trajectories. The method combines 2 distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity score matching. The propensity scores are used to balance observed covariates and the trajectory groups are used to control pretreatment measures of response. The trajectory groups also aid in characterizing classes of subjects for which no good matches are available. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montréal, Canada.