  We address discrete-marks survival analysis, also known as categorical sieve
analysis, for a setting of a randomized placebo-controlled treatment
intervention to prevent infection by a pathogen to which multiple exposures are
possible, with a finite number of types of "failure". In particular, we address
the case of interventions that are partially efficacious due to a combination
of failure-type-dependent efficacy and subject-dependent efficacy, for an
intervention that is "non-leaky" (where "leaky" interventions are those for
which each exposure event has a chance of resulting in a "failure" outcome, so
multiple exposures to pathogens of a single type increase the chance of
failure). We introduce the notion of some-or-none interventions, which are
completely effective only against some of the failure types, and are completely
ineffective against the others. Under conditions of no intervention-induced
failures, we introduce a framework and Bayesian and frequentist methods to
detect and quantify the extent to which an intervention's partial efficacy is
attributable to uneven efficacy across the failure types rather than to
incomplete "take" of the intervention. These new methods provide more power
than existing methods to detect sieve effects when the conditions hold. We
demonstrate the new framework and methods with simulation results and new
analyses of genomic signatures of HIV-1 vaccine effects in the STEP and RV144
vaccine efficacy trials.
