  Longitudinal studies are often subject to missing data. The ICH E9(R1)
addendum addresses the importance of defining a treatment effect estimand with
the consideration of intercurrent events. Jump-to-reference (J2R) is one
classically envisioned control-based scenario for the treatment effect
evaluation using the hypothetical strategy, where the participants in the
treatment group after intercurrent events are assumed to have the same disease
progress as those with identical covariates in the control group. We establish
new estimators to assess the average treatment effect based on a proposed
potential outcomes framework under J2R. Various identification formulas are
constructed under the assumptions addressed by J2R, motivating estimators that
rely on different parts of the observed data distribution. Moreover, we obtain
a novel estimator inspired by the efficient influence function, with multiple
robustness in the sense that it achieves $n^{1/2}$-consistency if any pairs of
multiple nuisance functions are correctly specified, or if the nuisance
functions converge at a rate not slower than $n^{-1/4}$ when using flexible
modeling approaches. The finite-sample performance of the proposed estimators
is validated in simulation studies and an antidepressant clinical trial.
