Data analysis
REGRESSION
A total of 217 patients were included in this study. Differences in baseline characteristics between participants who used the SMS system for all three RM products (Then, we developed latent trajectory models to identify clusters of individuals who shared similar trajectories in acceptability and adherence for each product (douche, insert, and suppository) over time. A form of multivariate mixture modeling macro, PROC TRAJ, which employs maximum likelihood to estimate model parameters, was utilized in the study [For latent trajectory analyses, we included a sample of participants who responded to their SMS system prompts and we tested one- to five-group quadratic trajectory models to find a best-fitting model. We first tested a quadratic trajectory model with one group as a general rule for data with three-time points [Finally, we manually created a “no temporal group” (participants who were not included in the latent trajectory analysis due to absence in SMS data) in addition to group membership which were driven by latent trajectory analysis and examined whether different RM group memberships were correlated with participants’ baseline sociodemographic characteristics and sexual behaviors. We also examined associations with acceptability. For participants who responded to acceptability (Q4) in the SMS system, we calculated the average acceptability score over the course of four weeks. We used participants’ acceptability scores from their PUEV survey if participants had not responded to the acceptability prompt in the SMS system. Fishers’ exact tests were used for categorical correlates and student’s t-tests were used for continuous correlates. In addition, multinomial logistic regression models were conducted to estimate the association between each trajectory and correlates. All analyses were conducted using SAS 9.4 (SAS Institute Inc., 2013).