The overall objective of this proposal is to develop powerful global statistical tests for comparing Parkinson disease treatments. Since no known biological marker of disease progression is identified, treatment efficacy is evaluated through multiple correlated outcomes. Conventional clinical trial designs are based on single primary outcomes. Such designs often either make clinical findings from other outcomes unclear (i.e., result in a lack of power to detect), or make sample size unnecessarily large in order to assure an appropriate small overall Type I error. In recent years, there is a growing demand for cost-effective clinical trial designs and efficient data analyses. A global statistical test (GST) is a single test which can """"""""squeeze"""""""" significance out of many single non-significant tests to enhance the power of the overall test. Several GSTs have recently been introduced into the literature, but there are limitations to the applicability of these GSTs to Parkinson disease treatment comparisons. We propose to develop new global statistical tests, which are particularly suitable for Parkinson disease treatment comparisons and are more powerful in detecting specified clinically meaningful treatment differences than tests based on single outcomes. We will 1. Develop nonparametric global statistical tests for comparing treatments in Parkinson disease clinical trials with multiple correlated outcomes; 2. Develop large sample properties of the global statistical tests and evaluate their small sample behaviors; 3. Develop sequential Parkinson disease clinical trial designs using global statistical tests; 4. Apply global statistical tests in Parkinson clinical trials; 5. Develop statistical software to facilitate the application of global statistical tests in Parkinson disease clinical trials.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21NS043569-02
Application #
6625778
Study Section
Special Emphasis Panel (ZNS1-SRB-K (04))
Program Officer
Oliver, Eugene J
Project Start
2002-03-15
Project End
2005-02-28
Budget Start
2003-03-01
Budget End
2005-02-28
Support Year
2
Fiscal Year
2003
Total Cost
$107,250
Indirect Cost
Name
Medical University of South Carolina
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
183710748
City
Charleston
State
SC
Country
United States
Zip Code
29425
Huang, Peng; Ou, Ai-hua; Piantadosi, Steven et al. (2014) Formulating appropriate statistical hypotheses for treatment comparison in clinical trial design and analysis. Contemp Clin Trials 39:294-302
Huang, Peng; Chen, Ming-Hui; Sinha, Debajyoti (2009) A latent model approach to define event onset time in the presence of measurement error. Stat Interface 2:425-435
Huang, Peng; Goetz, Christopher G; Woolson, Robert F et al. (2009) Using global statistical tests in long-term Parkinson's disease clinical trials. Mov Disord 24:1732-9
Clancy, Dawn E; Huang, Peng; Okonofua, Eni et al. (2007) Group visits: promoting adherence to diabetes guidelines. J Gen Intern Med 22:620-4
Clancy, Dawn E; Yeager, Derik Edward; Huang, Peng et al. (2007) Further evaluating the acceptability of group visits in an uninsured or inadequately insured patient population with uncontrolled type 2 diabetes. Diabetes Educ 33:309-14
Huang, Peng; Tilley, Barbara C; Woolson, Robert F et al. (2005) Adjusting O'Brien's test to control type I error for the generalized nonparametric Behrens-Fisher problem. Biometrics 61:532-9