Data analysis
REGRESSION, SECONDARY

Descriptive and bivariate (2-tailed independent t-tests; chi square tests) statistics were used to compare baseline sample characteristics of index and alter participants in the control versus intervention arms, in separate analyses. To examine intervention effects on index participant engagement in CC prevention advocacy, and alter participant uptake of CC screening, as well as other index and alter outcomes, we conducted multiple linear regression analysis for continuous outcomes (e.g., CC prevention advocacy) and multiple logistic regression analysis for CC screening. In each model, the month 6 measure of the outcome was the dependent variable, while independent variables included the baseline measure of the dependent variable (except for the outcome of alter CC screening, since all alter participants had never been screened for CC prior to enrollment), and an indicator of study arm, as well as covariates (age < 36 years, any secondary education, presence of a main sex partner, HIV status). If a measure was missing at month 6, the baseline measure of the variable was used to replace the missing value; for CC screening, missing data at month 6 was classified as not screened, which is similar to an intent-to-treat approach. All regression models involving alter data controlled for clustering at the level of each index (i.e., all the alters recruited by a specific index, as well as the index participant themselves, represent a single cluster) by using a SurveyReg or SurveyLogistic routine in SAS 9.2.