Abstract
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 discontinuation and initiation of symptomatic treatment. Different estimand strategies are discussed; 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).
Supplementary Materials
The supplementary materials describe data analyses of a cohort study from the Parkinson’s Progression Markers Initiative (PPMI) (Marek et al. 2011) which informed the simulation study.
Acknowledgments
We thank Annabelle Monnet and Judith Anzures-Cabrera from Roche and Khadija Rantell and Sabine Lenton from the UK Medicines and Healthcare products Regulatory Agency (MHRA) for helpful comments on an earlier draft of the manuscript which helped to improve the final presentation.
Data Availability Statement
Data for the supplementary analyses which informed the simulation study were obtained from the Parkinson’s Progression Markers Initiative (PPMI) (www.ppmi-info.org/, download of data on 30 Nov2020). Qualified researchers may obtain access to the full breadth of individual-level PPMI data via www.ppmi-info.org/access-data-specimens/download-data.
Disclosure statement
The authors report that there are no competing interests to declare.