Abstract:Noninferiority trials are used to assess whether the effect of a new drug is not worse than an active comparator by more than a noninferiority margin. If the difference between the new drug and the active comparator does not exceed this prespecified margin, noninferiority can be concluded. This margin must be specified based on clinical and statistical reasoning; however, it is considered as one of the most challenging steps in the design of noninferiority trials. Regulators recommend that the margin should be… Show more
“…Several approaches have been suggested to calculate the noninferiority margin. One common approach is the fixed‐margin method based on the effect of the active control in historical studies . The value recommended in the FDA guidance is the lower bound of the 95% CI of the estimated effect of a single placebo‐controlled trial or a meta‐analysis of such trials.…”
Section: Discussionmentioning
confidence: 99%
“…One common approach is the fixed-margin method based on the effect of the active control in historical studies. 1,41 The value recommended in the FDA guidance is the lower bound of the 95% CI of the estimated effect of a single placebo-controlled trial or a meta-analysis of such trials. Alternatively, a specific proportion of the observed treatment effect of the active control is used as the margin, which aims to reflect the largest loss of effect that would be clinically acceptable.…”
New treatments that are noninferior or equivalent to—but not necessarily superior to—the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient‐reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.
“…Several approaches have been suggested to calculate the noninferiority margin. One common approach is the fixed‐margin method based on the effect of the active control in historical studies . The value recommended in the FDA guidance is the lower bound of the 95% CI of the estimated effect of a single placebo‐controlled trial or a meta‐analysis of such trials.…”
Section: Discussionmentioning
confidence: 99%
“…One common approach is the fixed-margin method based on the effect of the active control in historical studies. 1,41 The value recommended in the FDA guidance is the lower bound of the 95% CI of the estimated effect of a single placebo-controlled trial or a meta-analysis of such trials. Alternatively, a specific proportion of the observed treatment effect of the active control is used as the margin, which aims to reflect the largest loss of effect that would be clinically acceptable.…”
New treatments that are noninferior or equivalent to—but not necessarily superior to—the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient‐reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.
“…• ݊ ଵ , ݊ be the sample sizes, with allocation ratio ݎ = ݊ ଵ /݊ . Several recommendations have been given regarding choice of the most appropriate noninferiority margin 3,6 , involving both clinical and statistical considerations. Whilst sample size calculations allow for stochastic variation between the true control event risk ߨ and its final observed estimate ߨ ො , they do not allow for substantial misjudgment in the envisaged truth.…”
to slightly inflate type I error rate; a solution is to use a lower significance level for testing, although this modestly reduces power.When working on the risk ratio scale instead, the same approach based on the modification of the margin leads to power levels above the nominal one, maintaining type I error under control.
Conclusions.Our proposed methods of designing non-inferiority trials using powerstabilising non-inferiority frontiers make trial design more resilient to unexpected values of the control event risk, at the only cost of requiring larger sample sizes when the goal is to report results on the risk difference scale.
“…Noninferiority (NI) trials are prevalent in many therapeutic areas and are used to evaluate new drugs [1e4]. When reading NI trials, one must pay special attention to the justification for the NI margin [5,6]. With the NI margin being a key part of NI trial design, any questionable justification for the choice of the NI margin can lead to incorrectly declaring a new drug a reasonable alternative to the current standard therapy.…”
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