  Clinical trials with longitudinal outcomes typically include missing data due
to missed assessments or structural missingness of outcomes after intercurrent
events handled with a hypothetical strategy. Approaches based on Bayesian
random multiple imputation and Rubin's rule for pooling results across multiple
imputed datasets are increasingly used in order to align the analysis of these
trials with the targeted estimand. We propose and justify deterministic
conditional mean imputation combined with the jackknife for inference as an
alternative approach. The method is applicable to imputations under a
missing-at-random assumption as well as for reference-based imputation
approaches. In an application and a simulation study, we demonstrate that it
provides consistent treatment effect estimates with the Bayesian approach and
reliable frequentist inference with accurate standard error estimation and type
I error control. A further advantage of the method is that it does not rely on
random sampling and is therefore replicable and unaffected by Monte Carlo
error.
