Lead Investigator: Günter Höglinger, Hanover Medical School
Title of Proposal Research: Prediction model for individual disease progression in patients with progressive supranuclear palsy
Vivli Data Request: 5485
Funding Source: None
Potential Conflicts of Interest: Günter Höglinger has served on the advisory boards for AbbVie, Alzprotect, Asceneuron, Biogen, Novartis, Roche, Sanofi, UCB; has received honoraria for scientific presentations from Abbvie, Biogen, Roche, Teva, UCB, has received research support from CurePSP, the German Academic Exchange Service (DAAD), German Parkinson’s Disease Foundation (DPG), German PSP Association (PSP Gesellschaft), German Research Foundation (DFG) and the German Ministry of Education and Research (BMBF), International Parkinson’s Fonds (IPF); has received institutional support from the German Center for Neurodegenerative Diseases (DZNE).
Potential conflicts of interests will be stated in any publication connected with this research proposal.
Summary of the Proposed Research:
Objective: To develop a computational model to predict the individual disease progression and to evaluate individual treatment effects of future therapeutic strategies in patients with progressive supranuclear palsy (PSP).
Background: PSP is a neurodegenerative disease with a mean survival time of 6-9 years after onset.1,2 A prevalence of 3 to 6 per 100,000 has been described,2,3 however, it is thought to be underestimated, because many patients remain undiagnosed.4 Disease manifestation, progression and duration varies greatly across patients with PSP.1,4
Individual prediction of disease progression in patients with PSP would be highly valuable to improve the patients´ quality of care and
to tailor endpoints of interventional trials in relation to criteria for inclusion of patients reflecting different risks for disease progression. .
Methods: The requested trial data will be included in a research synthesis on individual patient data. This research will also include high quality data from two natural history studies. The algorithm for the prediction model will be developed with R and PythonTM, using longitudinal, multimodal data. These include demographic data, clinical data, radiological data,5 fluid biomarkers, and risk single nucleotide polymorphisms (SNPs) for PSP.6,7 The annual change in the PSP Rating Scale (PSP-RS),8 and – when applicable – disease duration until death, will serve as measure for disease progression. Joint models will be developed to utilitze the the longitudinal data from the various outcome parameters, e.g, by using non-linear mixed effect model and estimating the parameter of interest by marginal maximum likelihood estimation.
These more modern approaches to modelling will be compared to standard (meta-)regression modelling allowing for the assessment of heterogeneity as introduced by study, region and center in addition to baseline patient characteristics.
Predictors as identified in these models will then be investigated regarding their ability to also predict longer-term outcome by means of matching patients in risk categories to respective patients in the natural history studies.
Qualitative and quantitative conclusions from the more modern and the standard statistical approaches for modeling and prediction will be critically discussed.
A Study to Assess Efficacy, Safety, Tolerability, and Pharmacokinetics of ABBV-8E12 in Subjects With Progressive Supranuclear Palsy (PSP)
Study ID: NCT02985879
Sponsor ID: M15-562
Update: This data request was withdrawn on 24 November 2020 by the researcher.