Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide that mainly affects motor system. Treatment given to PD patients may have further complications effect such as dyskinesias. People with PD receiving medication often experience complications. It is interest to identify factors associated with the complications. Data on 215people with PD obtained from the Parkinson's Progression Markers Initiative (PPMI) database were analysed. Total scores for the Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS) Part 1, 2, and 3 were used as the explanatory variables. We proposed Zero-Inflated Poisson (ZIP) to model the frequency of motor complications in people with PD. Therefore, the parameters in ZIP regression were estimated using Bayesian approach. Sampling from the posterior distribution of the parameters is conducted using Monte Carlo Markov Chain-Gibbs Sampling (MCMC-GS). The result shows that total score of MDS-UPDRS Part 1 and 2 are negatively associated with people for no need medication while the opposite is observed for total score of Part 3. Furthermore, in the second stage of the model, total score of Part 3is negatively associated with frequency of complications, while the opposite trend is observed for total score of Part 1 and 2.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 4 Dec 2018|
|Event||2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018 - Surabaya, Indonesia|
Duration: 21 Jul 2018 → …