Power and sample size determination for noninferiority trials using an exact method

Considerations in the design of clinical trials for pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy. Although outcomes for children with ALL have improved dramatically over the last 50 years, ALL remains the leading cause of childhood cancer death. In addition, high-risk patient subsets can be identified with significantly inferior survival. In the current era of therapies directed at specific molecular targets, the use of conventional randomized Phase III trials to show benefit from a new treatment regimen may not be feasible when these biologically defined subsets are small. This review presents the traditional approaches to designing trials for children with ALL, as well as innovative approaches attempting to study the benefit of new treatments as reliably as possible for patient subsets with distinctive biological characteristics.

Controlling the type 1 error rate in non-inferiority trials

Two different approaches have been proposed for establishing the efficacy of an experimental therapy through a non-inferiority trial: The fixed-margin approach involves first defining a non-inferiority margin and then demonstrating that the experimental therapy is not worse than the control by more than this amount, and the synthesis approach involves combining the data from the non-inferiority trial with the data from historical trials evaluating the effect of the control. In this paper, we introduce a unified approach that has both these approaches as special cases and show how the parameters of this approach can be selected to control the unconditional type 1 error rate in the presence of departures from the assumptions of assay sensitivity and constancy. It is shown that the fixed-margin approach can be extremely inefficient and that it is always possible to achieve equivalent control of the unconditional type 1 error rate, with higher power, by using an appropriately chosen synthesis method.

Designs of Superiority and Noninferiority Trials for Binary Responses are Noninterchangeable

To better understand the design of noninferiority trials for binary data, we identify analogies and contrasts between this and the more familiar superiority trial design. We restrict attention to the problem of detecting a difference between experimental and control response rates in the setting where there is no difference (piE - piC = 0) under the noninferiority alternative hypothesis and under the superiority null, and a matching difference between groups under the complementary hypotheses (/piE - piC/ = delta). Our derivation of the constrained maximum likelihood estimates (MLEs) reveals that superiority and noninferiority trials have different nuisance parameters--the marginal response rate and the control-group response rate, respectively. Our empirical results show that when individuals are allocated to treatment groups in the ratio that minimizes the overall sample size, balanced allocation is optimal only for superiority trials when the error rates are equal; otherwise imbalanced allocation is optimal. Different allocation ratios between trial types lead to different variances, and thus to different sample sizes. Finally, since the value of the marginal response rate--a design parameter in noninferiority trials--typically cannot be obtained from preliminary or published studies, we suggest a means of identifying a value that can be used. We conclude that full documentation of the design of a trial requires specification not only of the design parameters but also of the allocation ratio and the nuisance parameter, the value of which is not obvious under unequal allocation.

Statistical considerations for noninferiority/equivalence trials in vaccine development

Noninferioritylequivalence designs are often used in vaccine clinical trials. The goal of these designs is to demonstrate that a new vaccine, or new formulation or regimen of an existing vaccine, is similar in terms of effectiveness to the existing vaccine, while offering such advantages as easier manufacturing, easier administration, lower cost, or improved safety profile. These noninferioritylequivalence designs are particularly useful in four common types of immunogenicity trials: vaccine bridging trials, combination vaccine trials, vaccine concomitant use trials, and vaccine consistency lot trials. In this paper, we give an overview of the key statistical issues and recent developments for noninferioritylequivalence vaccine trials. Specifically, we cover the following topics: (i) selection of study endpoints; (ii) formulation of the null and alternative hypotheses; (iii) determination of the noninferioritylequivalence margin; (iv) selection of efficient statistical methods for the statistical analysis of noninferioritylequivalence vaccine trials, with particular emphasis on adjustment for stratification factors and missing pre-or post-vaccination data; and (v) the calculation of sample size and power.