Consistency ensured test strategies for supportive evidence of treatment efficacy in noninferiority clinical trials

Noninferiority (NI) clinical trials are designed to demonstrate that a new treatment is not unacceptably worse than an active control on a clinically meaningful endpoint. While such an endpoint can be of any type, the focus of this manuscript is on the binary-type endpoint. Examples of this endpoint can be clinical cure endpoint for patients with bacterial diseases or based on a pre-specified virological threshold for viral diseases. However, in addition to assessing such a binary endpoint for the NI comparison, the trial may also evaluate a second clinically relevant endpoint for providing additional support to the evidence of the designated primary endpoint. Specifically, if the trial is successful in demonstrating statistical significance on the first endpoint, then observing at least a positive trend in efficacy on the second endpoint may provide additional supportive evidence of efficacy. The second endpoint can be a time-to-event type endpoint, such as time-to-symptom resolution (TSR) or time to all-cause mortality for infectious disease trials, time-to-wound closure for wound healing trials, or other endpoints. We propose two consistency ensured test strategies for the two hypotheses of a trial, one associated with the binary endpoint and the other with the second endpoint, both with the objective of drawing inference regarding the efficacy of the new treatment based on findings from testing the two hypotheses. A key feature of these test strategies is that basically it does not require multiplicity adjustment of the significance levels. We conclude with general discussion of the testing methods and possible applications to unmet medical need trials.

Noninferiority studies with multiple reference treatments

The increasing popularity of noninferiority trials reflects the ongoing efforts to replace existing treatments (reference treatments) with new treatments (experimental treatments) that retain a substantial fraction of the effect of the reference treatments. The adoption of any new treatment has to be vindicated by a demonstration of benefits that outweigh a possible clinically insignificant reduction in the reference treatment efficacy. Statistical methods have been developed to analyze data collected from noninferiority trials. However, these methods focus on cases with only one reference treatment. In this paper, we provide the statistical inferential procedures for situations with multiple reference treatments. The computation of the corresponding critical values for simultaneous testings of noninferiority of several new treatments to multiple reference treatments in the presence of a placebo is provided. Furthermore, for a prespecified level of test power, a technique to determine the optimal sample size before the onset of a noninferiority trial is derived. A clinical example is given to illustrate our proposed procedure.