Analysis
HIV seroconversion
REGRESSION
We calculated descriptive statistics for sociodemographic characteristics and other variables of interest and used chi-square tests (for categorical covariates) and Kruskal–Wallis tests (for continuous covariates) to assess differences in sociodemographic and network characteristics between intervention and control arms.We calculated hazard ratios (HR) with 95% confidence intervals (CI) by employing Cox regression analysis (with a frailty term to fit a random intercept for peer networks) to assess the extent to which time to HIV seroconversion between intervention and control arms differed by network gender composition and index gender. Model 1 included an interaction term between study arm (intervention vs control) and network gender composition, including index (female vs male); and Model 2 included an interaction term between study arm (intervention vs control) and index gender (female vs male). For significant interaction terms, we calculated simple effects to explore the nature of the interaction and generated survival functions stratified by experimental arm using Kaplan–Meier methods to plot and compare time to failure (i.e., HIV seroconversion). We compared corresponding survival curves (e.g., intervention participants with 2 network females compared to control participants with 2 network females) using log-rank tests.We used several methods to assess the proportional hazards assumption. We graphed separate Kaplan–Meier survival functions for each categorical covariate. Visual inspection of the graphs showed parallel survival functions that diverged slowly over time and did not cross. We plotted scaled Schoenfeld residuals by time for all covariates and inspected the cumulative sum of residuals (score process pattern) with the corresponding results for a random selection of 20 out of 1000 simulated score process patterns. We then performed Kolmogorov-type supremum tests for all covariates to assess departure of the observed score process from the simulated ones and found none to be significant. Next, we generated time-dependent covariates by creating interaction terms between survival time and each covariate and added them to the models and found none to be significant. Finally, linear hypothesis testing and the score test yielded non-significant results. All of these procedures indicated that the proportional hazards assumption held. We calculated descriptive statistics in Stata (StataCorp, College Station, TX) and performed all other analyses in SAS (SAS Institute, Cary NC).