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, but not 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. Surprisingly, 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. Copyright 2009, Oxford University Press.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 96 (2009)
Issue (Month): 2 ()
Contact details of provider:
Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
Fax: 01865 267 985
Web page: http://biomet.oxfordjournals.org/
Other versions of this item:
- Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2008. "Optimal testing of multiple hypotheses with common effect direction," IEW - Working Papers 307, Institute for Empirical Research in Economics - University of Zurich.
- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Jeffrey Penney, 2013.
"Hypothesis Testing for Arbitrary Bounds,"
1319, Queen's University, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.