  Estimating population-level effects of a vaccine is challenging because there
may be interference, i.e., the outcome of one individual may depend on the
vaccination status of another individual. Partial interference occurs when
individuals can be partitioned into groups such that interference occurs only
within groups. In the absence of interference, inverse probability weighted
(IPW) estimators are commonly used to draw inference about causal effects of an
exposure or treatment. Tchetgen Tchetgen and VanderWeele (2012) proposed a
modified IPW estimator for causal effects in the presence of partial
interference. Motivated by a cholera vaccine study in Bangladesh, this paper
considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator
to the setting where the outcome is subject to right censoring using inverse
probability of censoring weights (IPCW). Censoring weights are estimated using
proportional hazards frailty models. The large sample properties of the IPCW
estimators are derived, and simulation studies are presented demonstrating the
estimators' performance in finite samples. The methods are then used to analyze
data from the cholera vaccine study.
