Statistical Analyses
HIV seroconversion
SECONDARY
Data from July 30, 2021, to April 28, 2022, were analyzed. A sample of 500 MSM from the 10 clusters (50 from each cluster), assuming a 30% dropout rate and an intracluster correlation of .020 (usually between .010 to .030) within 10 clusters, was planned to provide 85% power at 2-sided significance level of α=.05 to detect a 20% difference in the primary outcome. Descriptive analysis was used to describe baseline demographic characteristics for the total sample and by the intervention condition. The cumulative HIV testing uptake, incident testers (the first HIV test during the study period), HIV seroconversion, and HIV self-testing kit use were summarized. We assessed the difference in the proportion of HIV testing between study arms with 2-tailed For the sensitivity analysis, we conducted a per-protocol analysis including only participants who self-reported that they had viewed the intervention materials or had participated in our WeChat groups. For the prespecified analysis, we tested the interaction effect of the intervention between the individual level and community level. After the 6-month follow-up, we evaluated the effectiveness of the digital materials using data collected from the 3-month and 6-month follow-ups, and we evaluated the effectiveness of providing digital materials plus self-test kits using data collected from the 9-month and 12-month follow-ups. We also evaluated the effect of every component of the overall intervention, including viewing intervention images, viewing intervention videos, receiving HIV self-testing kits from the study team, and participating in the WeChat groups. In addition, we performed a subgroup analysis based on age (<30 years vs ≥30 years). We compared the demographic characteristics between participants who completed the last follow-up survey or seroconverted and participants who were lost to follow-up, and we adjusted the factors that differed between them in the analysis evaluating the intervention effect. We also used multiple imputations by chained equations and 30 imputed data sets to impute missing HIV testing data at each follow-up period and examined the intervention effect. We conducted an intention-to-treat analysis to evaluate the secondary outcomes using GLMMs or LMMs. We performed all analyses with SAS.