Statistical Analysis
REGRESSION
The primary outcome of this study was the mean number of rapid HIV tests per person per year. We estimated a sample size of 1500 participants (500 per group) was needed to detect a difference of 10% or more in the intervention groups, achieving 90% power at a 2-sided significance of 0.05. The sample size calculation assumed that those in the control group receive an average of 1 HIV test per year, based on a previous study on GSN app users in China [The mean number of rapid HIV tests per person per year in each group was calculated and analyzed using zero-inflated Poisson regression analysis, estimating the ratios of HIV testing rates in the 2 intervention groups compared to the control group, and reported as incident rate ratios (IRRs) and 95% CIs. The primary analysis was conducted with the intention-to-treat analysis. The proportion of participants who self-reported UAI at each follow-up (1 month, 3 months, and 6 months after randomization) was compared between the study groups using longitudinal, multilevel logistic regression models to account for repeated measures with each individual. The within-individual variable was time (baseline and follow-ups), and the interindividual variable was the intervention group. Adjusted multilevel logistic regression modeling evaluated differences in UAI among study groups. UAI analyses were done with the modified intention-to-treat analyses, excluding those who were never followed.Descriptive statistics for demographic characteristics, HIV testing history, and sexual risk behaviors were calculated using baseline data and then compared with chi-square tests for categorical variables between those who finished at least one follow-up and those who did not. All statistical analyses were performed with SAS software (version 9.3; SAS Institute, Inc.), and a 