Biometrika Advance Access originally published online on April 21, 2009
Biometrika 2009 96(2):399-410; doi:10.1093/biomet/asp006
Article |
Optimal testing of multiple hypotheses with common effect direction
Bittman Biostat, Inc., Glencoe, Illinois 60022, U.S.A. rmb{at}bittmanbiostat.com
Department of Statistics, Stanford University, Stanford, California 94305, U.S.A. romano{at}stanford.edu
Analytical Science, Takeda Global Research and Development, Deerfield, Illinois 60015, U.S.A. cvallarino{at}tpna.com
Institute for Empirical Research in Economics, University of Zurich, CH-8006 Zurich, Switzerland mwolf{at}iew.uzh.ch
Received for publication 1 February 2007. Revision received 1 August 2008.
We 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.
Key Words: Closure method Consonance Familywise error rate Multiple endpoints Multiple testing O'Brien's method
References
-
Chi G. Y. H. Multiple testings: multiple comparisons and multiple endpoints. Drug Info. J. (1998) 32:1347S–62S.
Cox D. R. Regression models and life-tables (with Discussion). J. R. Statist. Soc. (1972) B 34:187–220.
Dormandy J. A., Charbonnel B., Eckland D. A., Erdmann E., Massi-Benedetti M., Moules I. K., Skene A. M., Tan M. H., Lefèbvre P. J., Murray G. D., Standl E., Wilcox R. G., Wilhelmsen L., Betteridge J., Birkeland K., Golay A., Heine R. J., Korányi L., Laakso M., Mokán M., et al. Secondary prevention of macrovascular events in patients with type 2 diabetes in the PROactive Study (PROspective pioglitAzone Clinical Trial In macroVascular Events): a randomised controlled trial. Lancet (2005) 366:1279–89.[CrossRef][Web of Science][Medline]
Finner H. Stepwise multiple test procedures and control of directional errors. Ann. Statist. (1999) 27:274–89.[CrossRef]
Freemantle N. How well does the evidence on pioglitazone back up researchers' claims for a reduction in macrovascular events? Br. Med. J. (2005) 331:836–8.
Hochberg Y., Tamhane A. Multiple Comparison Procedures (1987) New York: John Wiley.
Holm S. A simple sequentially rejective multiple test procedure. Scand. J. Statist. (1979) 6:65–70.
Lachin J. M. Biostatistical Methods: The Assessment of Relative Risks (2000) New York: John Wiley.
Lehmacher W., Wassmer G., Reitmeir P. Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate. Biometrics (1991) 47:511–21.[CrossRef][Web of Science][Medline]
Lehmann E. L., Romano J. P. Testing Statistical Hypotheses (2005) 3rd ed. New York: Springer.
Liang K. Y., Zeger S. L. Longitudinal data analysis using generalized linear models. Biometrika (1986) 73:13–22.
Marcus R., Peritz E., Gabriel K. R. On closed testing procedures with special reference to ordered analysis of variance. Biometrika (1976) 63:655–60.
O'Brien P. C. Procedures for comparing samples with multiple endpoints. Biometrics (1984) 40:1079–87. Correction (1995), 51, 1580–1.[CrossRef][Web of Science][Medline]
Pocock S. J., Geller N. L., Tsiatis A. A. The analysis of multiple endpoints in clinical trials. Biometrics (1987) 43:487–98.[CrossRef][Web of Science][Medline]
Romano J. P., Wolf M. Exact and approximate stepdown methods for multiple hypothesis testing. J. Am. Statist. Assoc. (2005) 100:94–108.[CrossRef][Web of Science]
Shaffer J. P. Control of directional errors with stagewise multiple test procedures. Ann. Statist. (1980) 8:1342–7.[CrossRef]
Shaffer J. P. Optimality results in multiple hypothesis testing. In: The First Erich L. Lehmann Symposium – Optimality—Rojo J., Pérez-Abren V., eds. (2002) 44. Beachwood, Ohio: Inst. Math. Statist. 11–36. IMS Lecture Notes.
Sonnemann E. Allgemeine Lösungen multipler Testprobleme. EDV Medizin Biol. (1982) 13:120–8.
Sonnemann E., Finner H. Vollständigkeitssätze für multiple Testprobleme. In: Multiple Hypothesenprüfung—Bauer P., Hommel G., Sonnemann E., eds. (1988) Berlin: Springer. 121–35.
Wei L. J., Lin D. Y., Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J. Am. Statist. Assoc. (1989) 84:1065–73.[CrossRef][Web of Science]
Westfall P., Young S. Resampling-Based Multiple Testing. (1993) New York: John Wiley.
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