Assessing the non-inferiority of a new treatment in a three-arm trial with unknown coefficient of variation

Wu, Wei-Hwa; Hsieh, Hsin-Neng

doi:10.1080/03610918.2022.2051716

Cited by 1 publication

(2 citation statements)

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“…In Wu and Hsieh [ 5 ], when conducting non-inferiority test in a three-arm trial, they estimate the sample mean by Searls’ estimator (mean with CV) rather than the traditional one (pure sample mean), showing that testing results are better, in terms of empirical sizes and empirical powers. While in our research, different from the traditional three-arm trial, we conduct the non-inferiority test for the means with unknown CVs, and we show that the explicit inclusion of CVs in the measurement of non-inferiority can still control the type I error at the nominal level.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Casting aside the unbiasedness, Searls [ 4 ] proposed an estimator for mean that includes a known coefficient of variation (CV) in advance, which has a minimum mean square error. In Wu and Hsieh [ 5 ], through estimating the population mean of treatment effects in a three-arm rial by Searls’ estimator rather than traditional simple sample mean, they show that Searls’ estimator performs better, in terms of empirical size and empirical power. Thangjai et al [ 6 ] derives the expectation and variance of Searls’ estimator (with unknown CV).…”

Section: Introductionmentioning

confidence: 99%

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

Lee,

Wu,

et al. 2023

BMC Med Res Methodol

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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.

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Section: Conclusion and Discussionmentioning

confidence: 99%