Using global statistical tests in long-term Parkinson's disease clinical trials.
Parkinson's disease (PD) impairments are multidimensional, making it difficult to choose a single primary outcome when evaluating treatments to stop or lessen the long-term decline in PD. We review commonly used multivariate statistical methods for assessing a treatment's global impact, and we highlight the novel Global Statistical Test (GST) methodology. We compare the GST to other multivariate approaches using data from two PD trials. In one trial where the treatment showed consistent improvement on all primary and secondary outcomes, the GST was more powerful than other methods in demonstrating significant improvement. In the trial where treatment induced both improvement and deterioration in key outcomes, the GST failed to demonstrate statistical evidence even though other techniques showed significant improvement. Based on the statistical properties of the GST and its relevance to overall treatment benefit, the GST appears particularly well suited for a disease like PD where disability and impairment reflect dysfunction of diverse brain systems and where both disease and treatment side effects impact quality of life. In future long term trials, use of GST for primary statistical analysis would allow the assessment of clinically relevant outcomes rather than the artificial selection of a single primary outcome.
Huang
Peng
P
Division of Oncology Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA. phuang12@jhmi.edu
Goetz
Christopher G
CG
Woolson
Robert F
RF
Tilley
Barbara
B
Kerr
Douglas
D
Palesch
Yuko
Y
Elm
Jordan
J
Ravina
Bernard
B
Bergmann
Kenneth J
KJ
Kieburtz
Karl
K
D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D010300:Parkinson Disease / Q000628:therapy*