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

Estimation and inference for causal spillover effects in  egocentric-network randomized trials in the presence of network membership  misclassification