We analyzed whether a method for identifying latent trajectories—latent class growth analysis (LCGA)—was useful for understanding outcomes for individuals subject to an intervention. We used LCGA to reanalyze data from a published study of mentally ill homeless men in a critical time intervention (CTI) program. In that study, 96 men leaving a shelter's onsite psychiatric program were randomly assigned to experimental and control groups. The former received CTI services and the latter usual services. Each individual's housing circumstances were observed for 18 months after program initiation. Our outcome measure was monthly homelessness: a person was considered homeless in a month if he was homeless for even one night that month. Four latent classes were found among the control group, but just three among the experimental group. Control, but not experimental, group individuals showed a small class of chronically homeless men. The size of the never-homeless class was 19 percentage points larger for the experimental than for the control group. J- and inverted-U-shaped patterns were also found among both groups, but with important differences in timing of patterns. Our results reveal effects not apparent in the original analysis, suggesting that latent class growth models improve intervention evaluation.