Conducting the non-inferiority test for the means with unknown coefficient of variation in a three-arm trial

Author:

Lee Meng-Chih,Wu Wei-Ya,Lu Hung-Yi,Hsieh Hsin-Neng,Wu Wei-Hwa

Abstract

Abstract Background The non-inferiority test is a reasonable approach to assessing a new treatment in a three-arm trial. The three-arm trial consists of a placebo, reference, and an experimental treatment. The non-inferiority is often measured by the mean differences between the experimental and the placebo groups relative to the mean differences between the reference and the placebo groups. Methods To cope with possible estimation distortion due to the allowance of heteroskedasticity, we adjust the measurement of non-inferiority by the incorporation of coefficient of variation (CV) of the experimental, the reference and the placebo groups. In this research, we propose a generalized $$p$$ p -value based method (GPV-based method) to facilitate non-inferiority tests for the means with unknown coefficient of variation in a three-arm trial. Results The simulation results show that the GPV-based method can not only adequately control type I error rate at nominal level better but also provide power higher than those from Delta method and the empirical bootstrap method, which verifies the feasibility of our adjustment. Conclusions We revise the measurement of non-inferiority by deducting the CV of each kind of treatment from the average effect of trials. CVs are included in the non-inferiority explicitly to help prevent possible estimating distortion if heteroskedasticity is allowed. Through the simulation study, the performance of GPV-based method for facilitating non-inferiority tests for the means with unknown CV in a three-arm trial is better than those from empirical bootstrap method and Delta method for small, medium and large sample sizes. Hence, the GPV-based method is recommended to be used to conduct the non-inferiority test for the means with unknown CV in a three-arm trial. The GPV-based method still performs well in non-normality cases.

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Epidemiology

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