Optimal testing of multiple hypotheses with common effect direction
AbstractWe present a theoretical basis for testing related endpoints. Typically, it is known how to construct tests of the individual hypotheses, and the problem is how to combine them into a multiple test procedure that controls the familywise error rate. Using the closure method, we emphasize the role of consonant procedures, from an interpretive as well as a theoretical viewpoint. Suprisingly, even if each intersection test has an optimality property, the overall procedure obtained by applying closure to these tests may be inadmissible. We introduce a new procedure, which is consonant and has a maximin property under the normal model. The results are then applied to PROactive, a clinical trial designed to investigate the effectiveness of a glucose-lowering drug on macrovascular outcomes among patients with type 2 diabetes.
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Bibliographic InfoPaper provided by Institute for Empirical Research in Economics - University of Zurich in its series IEW - Working Papers with number 307.
Date of creation: Jul 2008
Date of revision:
Closure Method; Consonance; Familywise Error Rate; Multiple Endpoints; Multiple Testing; O’Brien’s method.;
Other versions of this item:
- Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2009. "Optimal testing of multiple hypotheses with common effect direction," Biometrika, Biometrika Trust, vol. 96(2), pages 399-410.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Joseph Romano & Michael Wolf, 2003.
"Exact and approximate stepdown methods for multiple hypothesis testing,"
Economics Working Papers
727, Department of Economics and Business, Universitat Pompeu Fabra.
- Joseph P. Romano & Michael Wolf, 2005. "Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 94-108, March.
- Jeffrey Penney, 2013.
"Hypothesis Testing for Arbitrary Bounds,"
1319, Queen's University, Department of Economics.
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