Assessing Individual and Disseminated Effects in Network-Randomized Studies

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, networklevel, randomized controlled human immunodeficiency virus (HIV) prevention trial conducted in the United States and Thailand from2002 to 2006 that recruited injection drug users, who were assigned to either an intervention group or a control group, and their risk networkmembers, 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 fromnetwork-randomized trials. Although the overall effect gains validity fromnetwork 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 nullan unfortunate and misleading conclusion.