  Estimands using the treatment policy strategy for addressing intercurrent
events are common in Phase III clinical trials. One estimation approach for
this strategy is retrieved dropout whereby observed data following an
intercurrent event are used to multiply impute missing data. However, such
methods have had issues with variance inflation and model fitting due to data
sparsity. This paper introduces likelihood-based versions of these approaches,
investigating and comparing their statistical properties to the existing
retrieved dropout approaches, simpler analysis models and reference-based
multiple imputation. We use a simulation based upon the data from the PIONEER 1
Phase III clinical trial in Type II diabetics to present complex and relevant
estimation challenges. The likelihood-based methods display similar statistical
properties to their multiple imputation equivalents, but all retrieved dropout
approaches suffer from high variance. Retrieved dropout approaches appear less
biased than reference-based approaches, resulting in a bias-variance trade-off,
but we conclude that the large degree of variance inflation is often more
problematic than the bias. Therefore, only the simpler retrieved dropout models
appear appropriate as a primary analysis in a clinical trial, and only where it
is believed most data following intercurrent events will be observed. The
jump-to-reference approach may represent a more promising estimation approach
for symptomatic treatments due to its relatively high power and ability to fit
in the presence of much missing data, despite its strong assumptions and
tendency towards conservative bias. More research is needed to further develop
how to estimate the treatment effect for a treatment policy strategy.
