Dynamic Prediction of Excessive Daytime Sleepiness Through Random Survival Forest: An application of the PPMI data

Abstract

Parkinson disease (PD) is the second most widespread neurodegenerative disease worldwide. Excessive daytime sleepiness (EDS) has a significant correlation in de novo PD patients. Identifying predictors is critical in order for early detection of disease diagnosis. We investigated clinical and biological markers related with time-dependent variables in sleepiness for early detection of PD. Data were obtained from the Parkinson’s Progression Markers Initiative study, which evaluates the progression markers in patients. The dataset also includes various longitudinal endogenous predictors. The measures of EDS were obtained through the Epworth Sleepiness Scale (ESS). Random survival forest method which can be deal with multivariate longitudinal endogenous predictors was used to predict the probability of having EDS in PD. The rate of having EDS among PD disease was 0.452. The OOB rate was 0.186. The VIMP and minimal depth indicated that the most important variables are stai state, JLO and the presence of ApoE4 Allele. In early PD, EDS is good indicator of the diagnosis of the PD and it increases over time and has association with several predictors.