  Parkinson's disease (PD) is a chronic, degenerative neurological disorder. PD
cannot be prevented, slowed or cured as of today but highly effective
symptomatic treatments are available. We consider relevant estimands and
treatment effect estimators for randomized trials of a novel treatment which
aims to slow down disease progression versus placebo in early, untreated PD. A
commonly used endpoint in PD trials is the MDS-Unified Parkinson's Disease
Rating Scale (MDS-UPDRS), which is longitudinally assessed at scheduled visits.
The most important intercurrent events (ICEs) which affect the interpretation
of the MDS-UPDRS are study treatment discontinuations and initiations of
symptomatic treatment. Different estimand strategies are discussed and
hypothetical or treatment policy strategies, respectively, for different types
of ICEs seem most appropriate in this context. Several estimators based on
multiple imputation which target these estimands are proposed and compared in
terms of bias, mean-squared error, and power in a simulation study. The
investigated estimators include methods based on a missing-at-random (MAR)
assumption, with and without the inclusion of time-varying ICE-indicators, as
well as reference-based imputation methods. Simulation parameters are motivated
by data analyses of a cohort study from the Parkinson's Progression Markers
Initiative (PPMI).
