To establish a rapid detection method for H7N9 avian influenza virus (AIV), monoclonal antibodies (mAbs) against hemagglutinin (HA) of H7N9 were developed to establish an antigen-capture enzyme-linked immunosorbent assay (AC-ELISA). AC-ELISA achieved high specificity and sensitivity, with a detection limit of 3.9 ng/mL for H7N9 HA protein (A/Zhejiang/DTID-ZJU01/2013), and 2-2 HA unit/100 μL for live H7N9 AIV. The inter- and intra-assay coefficient of variation was less than 10%. Compared with conventional virus isolation detection, the sensitivity and specificity were 94.96% and 88.24%, respectively. AC-ELISA proved to be a rapid and practical technique for the detection of H7N9 AIV.

A three-arm trial including an experimental treatment, an active reference treatment and a placebo is often used to assess the non-inferiority (NI) with assay sensitivity of an experimental treatment. Various hypothesis-test-based approaches via a fraction or pre-specified margin have been proposed to assess the NI with assay sensitivity in a three-arm trial. There is little work done on confidence interval in a three-arm trial. This paper develops a hybrid approach to construct simultaneous confidence interval for assessing NI and assay sensitivity in a three-arm trial. For comparison, we present normal-approximation-based and bootstrap-resampling-based simultaneous confidence intervals. Simulation studies evidence that the hybrid approach with the Wilson score statistic performs better than other approaches in terms of empirical coverage probability and mesial-non-coverage probability. An example is used to illustrate the proposed approaches.

The purpose of a non-inferiority trial is to assert the efficacy of an experimental treatment compared with a reference treatment by showing that the experimental treatment retains a substantial proportion of the efficacy of the reference treatment. Statistical methods have been developed to test multiple experimental treatments in three-arm non-inferiority trials. In this paper, we report the development of procedures that simultaneously test the non-inferiority and the superiority of experimental treatments after the assay sensitivity has been established. The advantage of the proposed test procedures is the additional ability to identify superior treatments while retaining an non-inferiority testing power comparable to that of existing testing procedures. Single-step and stepwise procedures are derived and then compared with each other to determine their relative testing power and testing error in a simulation study. Finally, the suggested procedures are illustrated with two clinical examples.

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.

Non-inferiority (NI) trials are becoming more popular. The NI of a new treatment compared with a standard treatment is established when the new treatment maintains a substantial fraction of the treatment effect of the standard treatment. A valid NI trial is also required to show assay sensitivity, the demonstration of the standard treatment having the expected effect with a size comparable to those reported in previous placebo-controlled studies. A three-arm NI trial is a clinical study that includes a new treatment, a standard treatment and a placebo. Most of the statistical methods developed for three-arm NI trials are designed for the existence of only one new treatment. Recently, a single-step procedure was developed to deal with NI trials with multiple new treatments with the overall familywise error rate controlled at a specified level. In this article, we extend the single-step procedure to two new step-up procedures for NI trials with multiple new treatments. A comparative study of test power shows that both proposed step-up procedures provide a significant improvement of power when compared to the single-step procedure. One of the two proposed step-up procedures also allows the flexibility of allocating different error rates between the sensitivity hypothesis and the NI hypotheses so that the assignment of fewer patients to the placebo becomes possible when designing NI trials. We illustrate the new procedures using data from a clinical trial.