摘要：Objectives. 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. Methods. 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 1 night that month. Results. Four latent classes were found among the control group, but just 3 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. Conclusions. Our results reveal effects not apparent in the original analysis, suggesting that latent class growth models improve intervention evaluation. Intervention research commonly estimates an impact of a particular intervention over a specified follow-up time period to identify average differences between intervention and control groups. However, intervention research into homelessness does not typically differentiate temporal patterns, i.e., patterns in sequencing, duration, or timing of the intervention’s impact among individuals. Ignoring such information might obscure or incorrectly estimate intervention effects. We analyzed the utility of latent class growth analysis (LCGA) 1 , 2 for differentiating such temporal patterns by reanalyzing data from a randomized critical time intervention (CTI) trial that was designed to prevent recurrent homelessness among men with mental disorders. The previous analysis 3 used an 18-month observation period to identify several temporal effects: the intervention group averaged 61 fewer nights homeless; it had half the number of homeless episodes; and its differences from the control group in the probability of extended homelessness grew over time. Our study carries the analysis further by demonstrating how LCGA can identify different, latent, temporal patterns among individuals resulting from the intervention. By using more of the temporal information in the data and by allowing individuals to vary simultaneously from themselves over time and from each other at each time point, our analysis portrays a more nuanced dynamic of temporal change than the prior analysis. As a result, it better specifies how subgroups, characterized by different dynamics, were differentially affected by CTI. In so doing, our analysis suggests the general utility of latent trajectory analysis for intervention research.
