  We introduce an extension of nonparametric DS inference for arbitrary
univariate CDFs to the case in which some failure times are (right)-censored,
and then apply this to the problem of assessing evidence regarding assertions
about relative risks across two populations. The approach enables exploration
of the sensitivity of survival analyses to assumed independence of the missing
data process and the failure proces. We present an application to the partially
efficacious RV144 (HIV-1) vaccine trial, and show that the strength of
conclusions of vaccine efficacy depend on assumptions about the maximum failure
rates of the subjects lost-to-followup.
