Efficient calculation of test sizes for non-inferiority
Félix Almendra-Arao
Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4138-4145
Abstract:
The nuisance parameter presents a serious computational obstacle to the calculation of test sizes in non-inferiority tests. This obstacle is the principal reason why studies performing unconditional non-inferiority tests calculate test sizes for only a few cases, only by simulation or with gross approximations. Typically, when fine approximations are made to calculate test sizes for non-inferiority tests, the calculation is made with the exhaustive method, which demands considerable computational effort. Although Newton’s method is generally more efficient than the exhaustive method, implementing the former requires that the first two derivatives of the power function have manageable closed forms. Unfortunately, for general critical regions, these derivatives have unmanageable representations. In this paper, we prove that when the critical regions are Barnard convex sets, the first two derivatives of the power function can take manageable closed forms, so Newton’s method can be applied to calculate the test sizes. Because of the rapid convergence of Newton’s method and the control that we have over the obtained precision, this method saves calculation time.
Keywords: Newton’s method; Non-inferiority tests; Test sizes; Proportions; Unconditional tests (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947311004051
Full text for ScienceDirect subscribers only.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4138-4145
DOI: 10.1016/j.csda.2011.11.008
Access Statistics for this article
Computational Statistics & Data Analysis is currently edited by S.P. Azen
More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().