Search

Often one of the primary objectives of an active-controlled noninferiority trial without a placebo arm is to assert that an experimental treatment would have been more effective than a putative placebo had the placebo been included in the trial. This may be an important consideration for regulatory applications. To achieve this objective, such a noninferiority analysis entails cross-trial statistical inference. Because of the uncertainty and difficulty surrounding cross-trial inference, the noninferiority analysis often aims to demonstrate that the experimental treatment preserves a specified fraction of the effect of the active control. The rationale is that by demonstrating the percent effect retention, the efficacy of the experimental treatment can be established with a great level of confidence. The confidence interval approach and synthesized test approach have been used for inferring the percent effect preservation. In this work we evaluate the type I error rates of these approaches to the cross-trial statistical inference for establishing treatment efficacy. The evaluation provides guidance as to what percentage of the control effect needs to be preserved so that through noninferiority testing of effect retention one can assert the treatment efficacy within a desired level of the error rate. [Copyright &y& Elsevier]

Increasingly often, the study objective in an active controlled clinical trial without a placebo arm is to show that a new treatment is no less effective than the active control treatment within some noninferiority range. Two issues behind this objective are that of whether the new treatment is efficacious relative to a putative placebo and that of whether the new treatment preserves a certain fraction of effect of the active control. To address these issues, two types of statistical analysis methods are employed in recent pharmaceutical applications. In one type of method, a noninferiority margin is determined, and then the relative effect of the new treatment versus the control is compared against the margin to test noninferiority and the efficacy of the new treatment. In the other type of method, a synthetic statistic is constructed to directly estimate or test the effect of the new treatment relative to the putative placebo without resorting to noninferiority argument. Preservation of control effect can also be estimated and tested. These methods carry some crucial assumptions. The effect of active control is often estimated from a collection of historical placebo controlled trials using the random effects modeling of DerSimonian and Laird. In this work we find that statistical validity of the latter method rests highly on the assumptions that control effect is not reduced in the current active controlled trial population compared to the historical trials and that a normal approximation is appropriate in the random effects modeling. This type of method is very sensitive to departure from these assumptions. In contrast, the former method is ultraconservative in terms of type I error when the assumptions are met and can be anticonservative when control effect is substantially less in the active controlled trial than estimated from the historical placebo controlled trials. [Copyright &y& Elsevier]

The article reports that the sample size during the course of a conventional fixed design clinical trial covers relevant points for the context of adjusting. By use of a nonconstant critical value perceived as an additional flexibility to control its conditional Type I error, the conventional Z test controls the unconditional Type I error.