Assessing Individual and Disseminated Effects in Network-Randomized Studies. Issue 11 (20th July 2018)

• Record Type: Journal Article
• Authors: Buchanan, Ashley L; Vermund, Sten H; Friedman, Samuel R; Spiegelman, Donna
• Is Part Of: American journal of epidemiology. Volume 187:Issue 11(2018)
• Page Start: 2449
• Page End: 2459
• Publication Date: 2018-07-20
• Subjects: causal inference -- cluster-randomized trials -- disseminated/indirect effects -- drug use/abuse -- HIV/AIDS -- implementation science -- individual/direct effects -- interference; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwy149
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Implementation trials often involve clustering via risk networks, where only some participants directly receive the intervention. The individual effect is that among directly treated persons beyond being in an intervention network; the disseminated effect is that among persons engaged with those directly treated. In this article, we employ a causal inference framework and discuss assumptions and estimators for individual and disseminated effects and apply them to the HIV Prevention Trials Network 037 Study. HIV Prevention Trials Network 037 was a phase III, network-level, randomized controlled human immunodeficiency virus (HIV) prevention trial conducted in the United States and Thailand from 2002 to 2006 that recruited injection drug users, who were assigned to either an intervention group or a control group, and their risk network members, who received no direct intervention. Combining individual and disseminated effects, we observed a 35% composite rate reduction in the adjusted model (risk ratio = 0.65, 95% confidence interval: 0.47, 0.90). Methodology is now available for estimating the full set of these effects, enhancing knowledge gained from network-randomized trials. Although the overall effect gains validity from network randomization, we show that it will generally be less than the composite effect. Additionally, if only index participants benefit from the intervention, as the network size increases, the overall effect tends toward the null—an unfortunate and misleading conclusion.