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

Nonparametric survival analysis and vaccine efficacy using Dempster-Shafer analysis