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Non‐inferiority trials: the ‘ at least as good as ’ criterion with dichotomous data
Author(s) -
Laster Larry L.,
Johnson Mary F.,
Kotler Mitchell L.
Publication year - 2006
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.2476
Subject(s) - interpretability , margin (machine learning) , statistics , sample size determination , econometrics , mathematics , univariate , computer science , artificial intelligence , machine learning , multivariate statistics
The ‘ at least as good as ’ criterion, introduced by Laster and Johnson for a continuous response variate, is developed here for applications with dichotomous data. This approach is adaptive in nature, as the margin of non‐inferiority is not taken as a fixed difference; it varies as a function of the positive control response. When the non‐inferiority margin is referenced as a high fraction of the positive control response, the procedure is seen to be uniformly more efficient than the fixed margin approach, yielding smaller sample sizes when sizing non‐inferiority trials under identically specified conditions. Extending this method to proportions is straightforward, but highlights special considerations in the design of non‐inferiority trials versus superiority trials, including potential trade‐offs in statistical efficiency and interpretability. Copyright © 2005 John Wiley & Sons, Ltd.