Assessing Individual and Disseminated Effects in Network-Randomized Studies

ABSTRACT

Implementation trials often involve clustering via risk networks, where only some participants directly received 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. We employ a causal inference framework and discuss assumptions and estimators for individual and disseminated effects and apply them to HIV Prevention Trials Network 037. HIV Prevention Trials Network 037 was a Phase III, network-level, randomized controlled HIV prevention trial conducted in the US and Thailand from 2002 to 2006 that recruited persons who injected drugs, who received either intervention or control, and their risk network members, who received no direct intervention. Combining individual and disseminated, a 35% composite rate reduction was observed in the adjusted model (95% confidence interval = 0.47, 0.90). Methodology is now available to estimate 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, in general, be less than the composite effect. Additionally, if only index participants benefit from the intervention, as the network size increases, the overall effect tends to the null, an unfortunate and misleading conclusion.