Trial Outcomes & Findings for eTest: Real-time, Remote Monitoring System for Home-based HIV Testing Among High-risk Men Who Have Sex With Men (NCT NCT03654690)

eTest: Real-time, Remote Monitoring System for Home-based HIV Testing Among High-risk Men Who Have Sex With Men

We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. We fit longitudinal mixed effects models for two outcomes, HIV testing and high-risk CAS events within a given follow-up period, given that these outcomes varied within participants across the study period. We specified distributions appropriate for each outcome (logistic for HIV testing and negative binomial for high-risk CAS events) with suitable link functions, unstructured covariance structures and robust standard errors. Time was included as a continuous covariate. A covariate reflecting pre-enrolment HIV testing and baseline CAS events were included in these models. We used an intent-to-treat approach for all analyses. Missing data were considered missing at random.

We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. We fit longitudinal mixed effects models for two outcomes, HIV testing and high-risk CAS events within a given follow-up period, given that these outcomes varied within participants across the study period. We specified distributions appropriate for each outcome (logistic for HIV testing and negative binomial for high-risk CAS events) with suitable link functions, unstructured covariance structures and robust standard errors. Time was included as a continuous covariate. A covariate reflecting pre-enrolment HIV testing and baseline CAS events were included in these models. We used an intent-to-treat approach for all analyses. Missing data were considered missing at random.

count of participants who were ultimately diagnosed with HIV during the course of the study

We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A binary variable reflecting whether participants had ever had a PrEP prescription in the past was included for the PrEP prescription model. We specified two-way interactions between these covariates and condition assignment in all models, but none were significant and were excluded.

We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. A dummy-coded covariate indicating whether participants reported testing fewer than three times in the 3 years prior to enrolling was included in all models of HIV testing. A similar covariate for STI testing was included in the STI testing model. We specified two-way interactions between these covariates and condition assignment in all models, but none were significant and were excluded.

We used logistic regression with dummy-coded condition assignment as a predictor to test differences in outcomes across experimental conditions. We specified two-way interactions between these covariates and condition assignment in all models, but none were significant and were excluded. We fit longitudinal mixed effects models for two outcomes, HIV testing and high-risk CAS events within a given follow-up period, given that these outcomes varied within participants across the study period. We specified distributions appropriate for each outcome (logistic for HIV testing and negative binomial for high-risk CAS events) with suitable link functions, unstructured covariance structures and robust standard errors. Time was included as a continuous covariate. A covariate reflecting pre-enrolment HIV testing and baseline CAS events were included in these models.