A novel approach to noninferiority testing: documented proof rather than arbitrary margins

Noninferiority trials have been criticized for their wide margins of noninferiority, making it virtually impossible to reject noninferiority. Recommendations have been given to replace the practice of arbitrarily set margins. The objective of this study was to review various alternative methods of assessment based on statistical reasoning. Four examples are given. (1) In a 300-patient parallel group study of 2 inhalers for asthma, noninferiority was demonstrated at P = 0.0001. This result was supported by both the lack of a significant difference between the standard and new inhalers and the presence of a significant difference between the new inhaler and a placebo at P = 0.0001. (2) In a 236-patient parallel group sleeping pill study, noninferiority was demonstrated at P = 0.04. The presence of noninferiority was supported by a significant superiority of the new compound against a placebo at P = 0.021. However, the significantly worse performance against the standard treatment undermined these findings. (3) In a 200-patient hypertension study of 2 treatment groups, noninferiority was demonstrated at P = 0.028. The presence of noninferiority was supported by the lack of a significant difference between the new and the standard treatment. However, these findings were undermined by the lacking superiority of the new compound against a placebo. (4) In a 160-patients parallel group cholesterol study, noninferiority was demonstrated at P = 0.01. The presence of noninferiority was undermined by both the significant difference between the new and the standard treatment and the lack of efficacy of the new treatment against a placebo. We conclude that expert investigators traditionally set an arbitrary margin of noninferiority based on clinical arguments and that they benefit from wide margins. As an alternative and more meaningful approach to noninferiority testing, we propose to use (1) margins based on counted rather than on arbitrary criteria, (2) null hypothesis tests between the new and standard treatments, (3) null hypothesis tests between the new treatment and a placebo.