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Controlling the type 1 error rate in non‐inferiority trials
Author(s) -
Snapinn Steven,
Jiang Qi
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3072
Subject(s) - type i and type ii errors , margin (machine learning) , computer science , word error rate , statistics , type (biology) , control (management) , econometrics , mathematics , artificial intelligence , machine learning , ecology , biology
Two different approaches have been proposed for establishing the efficacy of an experimental therapy through a non‐inferiority trial: The fixed‐margin approach involves first defining a non‐inferiority margin and then demonstrating that the experimental therapy is not worse than the control by more than this amount, and the synthesis approach involves combining the data from the non‐inferiority trial with the data from historical trials evaluating the effect of the control. In this paper, we introduce a unified approach that has both these approaches as special cases and show how the parameters of this approach can be selected to control the unconditional type 1 error rate in the presence of departures from the assumptions of assay sensitivity and constancy. It is shown that the fixed‐margin approach can be extremely inefficient and that it is always possible to achieve equivalent control of the unconditional type 1 error rate, with higher power, by using an appropriately chosen synthesis method. Copyright © 2007 John Wiley & Sons, Ltd.