  To leverage peer influence and increase population behavioral changes,
behavioral interventions often rely on peer-based strategies. A common study
design that assesses such strategies is the egocentric-network randomized trial
(ENRT), in which those receiving the intervention are encouraged to disseminate
information to their peers. The Average Spillover Effect (ASpE) measures the
impact of the intervention on participants who do not receive it, but whose
outcomes may be affected by others who do. The assessment of the ASpE relies on
assumptions about, and correct measurement of, interference sets within which
individuals may influence one another's outcomes. It can be challenging to
properly specify interference sets, such as networks in ENRTs, and when
mismeasured, intervention effects estimated by existing methods will be biased.
In HIV prevention studies where social networks play an important role in
disease transmission, correcting ASpE estimates for bias due to network
misclassification is critical for accurately evaluating the full impact of
interventions. We combined measurement error and causal inference methods to
bias-correct the ASpE estimate for network misclassification in ENRTs, when
surrogate networks are recorded in place of true ones, and validation data that
relate the misclassified to the true networks are available. We investigated
finite sample properties of our methods in an extensive simulation study, and
illustrated our methods in the HIV Prevention Trials Network (HPTN) 037 study.
