  This paper considers one-step targeted maximum likelihood estimation method
for general competing risks and survival analysis settings where event times
take place on the positive real line R+ and are subject to right-censoring. Our
interest is overall in the effects of baseline treatment decisions, static,
dynamic or stochastic, possibly confounded by pre-treatment covariates. We
point out two overall contributions of our work. First, our method can be used
to obtain simultaneous inference across all absolute risks in competing risks
settings. Second, we present a practical result for achieving inference for the
full survival curve, or a full absolute risk curve, across time by targeting
over a fine enough grid of points. The one-step procedure is based on a
one-dimensional universal least favorable submodel for each cause-specific
hazard that can be implemented in recursive steps along a corresponding
universal least favorable submodel. We present a theorem for conditions to
achieve weak convergence of the estimator for an infinite-dimensional target
parameter. Our empirical study demonstrates the use of the methods.
