title: Predictors of time to initiation of symptomatic therapy in early Parkinson's disease.
abstract: OBJECTIVE
  To determine clinical and biological variables that predict time to initiation of symptomatic therapy in de novo Parkinson's disease patients.
  
  
  METHODS
  Parkinson's Progression Markers Initiative is a longitudinal case-control study of de novo, untreated Parkinson's disease participants at enrolment. Participants contribute a wide range of motor and non-motor measures, including biofluids and imaging biomarkers. The machine learning method of random survival forests was used to examine the ability of baseline variables to predict time to initiation of symptomatic therapy since study enrollment (baseline).
  
  
  RESULTS
  There were 423 PD participants enrolled in PPMI and 33 initial baseline variables. Cross-validation results showed that the three-predictor subset of disease duration (time from diagnosis to enrollment), the modified Schwab and England activities of daily living scale, and the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score modestly predicted time to initiation of symptomatic therapy (C = 0.70, pseudo-R (2) = 0.13). Prediction using the three variables was similar to using the entire set of 33. None of the biological variables increased accuracy of the prediction. A prognostic index for time to initiation of symptomatic therapy was created using the linear and nonlinear effects of the three top variables based on a post hoc Cox model.
  
  
  CONCLUSIONS
  Our findings using a novel machine learning method support previously reported clinical variables that predict time to initiation of symptomatic therapy. However, the inclusion of biological variables did not increase prediction accuracy. Our prognostic index constructed, based on the group-level survival curve can provide an indication of the risk of initiation of ST for PD patients based on functions of the three top predictors.
authors.affiliation: Neurology Parkinson's Disease and Movement Disorders Center Northwestern University Chicago Illinois.
authors.forename: Tanya
authors.initials: T
authors.lastname: Simuni
authors.affiliation: University of Iowa iowa City Iowa.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Long
authors.affiliation: Clinical Trials Statistical & Data Management Center University of Iowa College of Public Health iowa City iowa.
authors.forename: Chelsea
authors.initials: C
authors.lastname: Caspell-Garcia
authors.affiliation: Clinical Trials Statistical & Data Management Center University of Iowa College of Public Health iowa City iowa.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Coffey
authors.affiliation: Institute for Neurodegenerative Disorders (IND) Molecular NeuroImaging LLC (MNI) New Haven Connecticut.
authors.forename: Shirley
authors.initials: S
authors.lastname: Lasch
authors.affiliation: Neurology University of California San Francisco California.
authors.forename: Caroline M
authors.initials: CM
authors.lastname: Tanner
authors.affiliation: Institute for Neurodegenerative Disorders (IND) Molecular NeuroImaging LLC (MNI) New Haven Connecticut.
authors.forename: Danna
authors.initials: D
authors.lastname: Jennings
authors.affiliation: Center for Human Experimental Therapeutics University of Rochester Rochester New York.
authors.forename: Karl D
authors.initials: KD
authors.lastname: Kieburtz
authors.affiliation: Institute for Neurodegenerative Disorders (IND) Molecular NeuroImaging LLC (MNI) New Haven Connecticut.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Marek
keywords: Biomarkers; Parkinson's disease; symptomatic therapy