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This article concerns methodology for testing the significance of differences in mean rates of change in controlled repeated measurements designs with limited sample sizes, autoregressive error structures, nonlinear patterns of underlying true mean change, dropout rates exceeding 50%, plus other missing data. Each of these is problematic for ordinary repeated measures analysis of variance, and a complex generalized linear mixed model formulation popularly advocated for the ability to deal with autoregressive error structures and missing data is shown to perform poorly in such circumstances. Monte Carlo simulation methods confirm that simple two-stage analyses of dropout-weighted linear slope coefficients provide conservative Type 1 error protection, although adequate power requires the presence of large treatment effects in studies with the limited sample sizes and high proportions of missing data. No other analysis has been documented to provide both conservative Type 1 error protection and competitive power under similarly taxing conditions.

Synthetic cannabinoids (SCs) are low-cost substances that have been associated with adverse health outcomes and an increase in emergency department visits over recent years, particularly among people experiencing homelessness. This mixed methods study explored the connection between homelessness, SC use, and readiness to quit in order to inform the development of harm reduction strategies. Individuals (18+) residing in homeless encampments in Houston, TX with experiences of SC use were eligible to participate. Participants ( N = 65) completed an interviewer-administered survey about their SC use. Most participants were Black/African American (65.7%), male (82.9%), and most (75.4%) reported using SCs to avoid positive drug tests. Many wanted to quit using SCs (69.2%) and already employed harm reduction strategies while using SCs. Organizations supporting individuals experiencing homelessness who use SCs should focus on reducing barriers to stopping SC use and increasing the availability of housing and supportive services.