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Assessment of Sensorimotor Function in Mouse Models of Parkinson's Disease

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Published on: June 17, 2013

A Transitional Probability Model for Parkinson's Disease Motor States With Applications to Missing Data.

¹1 Castro Valley, CA, USA.

Therapeutic Innovation & Regulatory Science

|September 19, 2018

Summary

A Markov transitional probability model helps predict Parkinson's disease motor fluctuations. This approach aids in understanding treatment effects, such as with carbidopa-levodopa, and can improve data analysis in clinical trials.

Keywords:

Markov model Parkinson’s disease missing data multiple imputation transitional probability

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

Area of Science:

Neuroscience
Clinical Pharmacology
Biostatistics

Background:

Parkinson's disease (PD) is a progressive neurodegenerative disorder causing significant disability.
Advanced PD patients often experience motor complications like fluctuations and dyskinesia, impacting daily life.
Levodopa (LD) is a primary PD treatment, but long-term use can lead to motor complications.

Purpose of the Study:

To propose a Markov transitional probability model for estimating state transitions in PD.
To apply this model to analyze clinical trial data for extended-release vs. immediate-release carbidopa-levodopa (CD-LD).

Main Methods:

Development of a Markov transitional probability model.
Application of the model to clinical trial data comparing extended-release and immediate-release CD-LD.

Main Results:

The model effectively estimates the likelihood of remaining in a specific motor state or transitioning between states.
Illustrates the model's utility in a clinical trial setting for evaluating different CD-LD formulations.

Conclusions:

Markov transitional probability models are valuable for quantifying state changes in PD.
The model can also serve as a basis for multiple imputation of missing clinical data.