Data analysis
REGRESSION, SECONDARY
As described elsewhere [Our analysis was designed prospectively and proceeded in 3 steps [Secondly, for each of the 20 clusters, the proportion of survey respondents with knowledge of their HIV status was calculated. We calculated the average value of the cluster-specific proportions of individuals who reported knowledge of their HIV status for each trial arm, using both the arithmetic and geometric means. We also estimated the coefficient of variation (k). Next, we calculated the prevalence ratio (PR), comparing the Yathu Yathu arm with the control arm, based on the ratio of the geometric means for each trial arm. To formally compare the trial arms in an “unadjusted” analysis, we then fitted a linear regression model of log (cluster-level proportion) on trial arm and community, in order to obtain a log(PR) comparing the Yathu Yathu arm with the control arm and a corresponding 95% confidence interval.In the third step, we followed the two-stage procedure for analysis of CRTs with <15 clusters per arm, in order to adjust for any imbalance between trial arms in survey participants’ sex and age group (and in sensitivity analysis, for additional individual-level characteristics) [We used the same methods to compare the secondary outcomes between the 2 trial arms. We also conducted prespecified subgroup analysis for each of the 4 sex and age group combinations (males and females aged 15 to 19 and 20 to 24 years). The two-stage analysis described above was repeated, but without adjustment for sex or age group at Stage 1. For the primary outcome, to explore whether there was statistical evidence for interaction by age and sex, we conducted an interaction test.