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Methods for Assessing Noninferiority with Censored Data
Author(s) -
Freitag Gudrun
Publication year - 2005
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200410083
Subject(s) - nonparametric statistics , margin (machine learning) , parametric statistics , contrast (vision) , null hypothesis , econometrics , statistics , mathematics , statistical hypothesis testing , computer science , artificial intelligence , machine learning
In this paper we present the existing approaches to the problem of showing noninferiority with randomly right censored data. The main focus is on the choice of the discrepancy measure which is used to define the deviation from the classical null hypothesis, i.e. the noninferiority margin. Most methods are based on certain parametric or semiparametric assumptions. In contrast, a new, completely nonparametric approach is suggested and discussed. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)