Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies.
Ashley L Buchanan, Ra��l U Hern��ndez-Ram��rez, Judith J Lok, Sten H Vermund, Samuel R Friedman, Laura Forastiere, Donna Spiegelman
Author Information
Ashley L Buchanan: From the Department of Pharmacy Practice and Clinical Research, College of Pharmacy, University of Rhode Island, Kingston, RI. ORCID
Ra��l U Hern��ndez-Ram��rez: Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT.
Judith J Lok: Department of Mathematics and Statistics, Boston University, Boston, MA.
Sten H Vermund: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT.
Samuel R Friedman: Department of Population Health, New York University Grossman School of Medicine, New York, NY. ORCID
Laura Forastiere: Department of Biostatistics, Yale School of Public Health, New Haven, CT. ORCID
Donna Spiegelman: Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT.
BACKGROUND: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. RESULTS: There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS: These methods will be useful for evaluating intervention packages in studies with network features.
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