Menza et al. BMC Public Health 2010, 10:774 http://www.biomedcentral.com/1471-2458/10/774 preliminary test of whether CM can be ultimately employed as an HIV prevention tool. We have pre- viously shown this metric to be associated with HIV acquisition [28]. The secondary substance use outcomes represent intermediate variables in the causal chain in which methamphetamine use may facilitate the high risk sex that in turn leads to HIV/STI transmission. Reliability of behavioral study endpoints We conducted a test-retest reliability study of the beha- vioral endpoints assessed in this trial [29]. From Decem- ber 2006 to March 2007, we enrolled 102 MSM recruited from an STI clinic and an HIV clinic to com- plete the ACASI employed in this study. Of the 102 men who completed an initial ACASI, 98 (96%) returned to complete a second ACASI in a median of 4 days (range: 2-5). Participants took no more than 38 minutes to complete the initial ACASI. We calculated reliabilities using kappa statistics for binary outcomes and intraclass correlation coefficients by repeated mea- sures analysis of variance (ANOVA) for continuous out- comes. The reliabilities of self-reported non-concordant UAI, weekly or more frequent use of methamphetamine, number of non-concordant UAI partners, and number of quarters of methamphetamine were 0.72 (95%CI: 0.47, 0.87), 0.88 (95%CI: 0.62, 0.99), 0.85 (95%CI: 0.78, 0.91), and 0.96 (95%CI: 0.94, 0.99), respectively. Statistical analyses The primary purpose of this trial was to gather data for the design of a phase IIb or III clinical trial. The study was originally designed to follow 60 individuals. Assum- ing that 14% of participants would report non-concor- dant UAI at baseline, this sample size was chosen to provide estimates of the proportion of men who report non-concordant UAI in the prior six weeks with an expected width of the 95% confidence interval (CI) of 0.085. We expected to lose 25% of our study population to follow-up and aimed to enroll 80 participants. Addi- tional resources, however, allowed us to enroll 127 participants. We used chi-squared tests and t-tests to compare retention at the 24-week study visit and CM interven- tion adherence, respectively, between groups of partici- pants defined by selected characteristics. We used generalized estimating equations (GEE) to estimate the proportion of participants reporting non-concordant UAI in the prior six weeks at each study visit. Analyses estimating the effectiveness of CM were intention to treat, on the basis of a binomial regression models with log links, to calculate relative risks comparing the pro- portion of men reporting non-concordant UAI between the study groups at study visits during the 12-week intervention and 12-week follow-up periods [30]. Our Page 4 of 13 analyses of secondary binary endpoints employed the same analytic methods. Finally, we used a GEE model with a log link and negative binomial errors to evaluate the intervention’s effect, expressed as a rate ratio, on the number of non-concordant UAI partners. We defined statistical significance at the P < 0.05 level. We pre-selected baseline covariates that we thought would most strongly predict our outcomes of interest for adjusted analyses, including predictors of retention at the 24-week study visit. Of these covariates, we adjusted for variables that resulted in at least a 10% change in the adjusted RR (aRR) compared to the unad- justed RR [31]. We chose such strict criteria because of our relatively small sample size and because several potential confounders differed between our intervention and control groups. All analyses included a covariate indicating the baseline value of the outcome of interest. Estimates of the proportion of men reporting non- concordantUAI at each visit and analyses comparing any non-concordant UAI and number of non-concor- dant UAI partners between CM and control were also adjusted for HIV status and use of other substances (inhaled nitrites, gamma-hydroxybutyrate, ecstasy, or erectile dysfunction medications) in the prior 6 weeks at baseline. Analysis of detection of methamphetamine use by urinalysis was also adjusted for stage of change for methamphetamine use at baseline. We note that dura- tion of methamphetamine use and injection use of methamphetamine were both evaluated as potential con- founders; their inclusion in models that already included the above variables produced similar results to analyses without these two predictors. We assessed participants’ stage of change for metham- phetamine use by a single question [32,33]: “Have you been trying to cut back or stop using methampheta- mine?” This question was added after the first 19 parti- cipants were enrolled as it was not a variable we had initially planned to collect. The pattern of missingness, however, is missing completely at random (MCAR) which is unlikely to introduce bias to complete case analyses like the one presented in this manuscript [34]. Results Enrollment, retention, and participant characteristics Over our 15-month enrollment period, we screened 222 men for eligibility (14.8/month) and enrolled and rando- mized 127 men (8.5/month). Of these randomized parti- cipants, 107 (84%) attended the 24-week study visit; retention at 6-week assessments was similar for both groups (Figure 1). HIV-negative men were less likely to attend the 24-week study visit than HIV-positive men (74% v. 93%, P < 0.01) and men who reported metham- phetamine use for ≥10 years were less likely to attend the 24-week study visit than men who reported