  In the context of right-censored and interval-censored data we develop
asymptotic formulas to compute pseudo-observations for the survival function
and the Restricted Mean Survival Time (RMST). Those formulas are based on the
original estimators and do not involve computation of the jackknife estimators.
For right-censored data, Von Mises expansions of the Kaplan-Meier estimator are
used to derive the pseudo-observations. For interval-censored data, a general
class of parametric models for the survival function is studied. An asymptotic
representation of the pseudo-observations is derived involving the Hessian
matrix and the score vector. Theoretical results that justify the use of
pseudo-observations in regression are also derived. The formula is illustrated
on the piecewise-constant-hazard model for the RMST. The proposed
approximations are extremely accurate, even for small sample sizes, as
illustrated on Monte-Carlo simulations and real data. We also study the gain in
terms of computation time, as compared to the original jackknife method, which
can be substantial for large dataset.
