Identification of Minimum Effective Dose based on Ratio of a Normally Distributed Data under Heteroscedasticity

E. D. Kpeglo, Michael J. Adjabui, J. Dioggban
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Abstract

Efficacy and safety study is of practical importance in modern drug development. It is a key component in evaluating the safety of food additives or pesticides, and assessing the effectiveness and safety of drugs. In most of the various statistical procedures, homogeneity of variances among different dose levels was required. This paper without a need for multiplicity adjustment proposes a stepwise confidence set procedure for estimating Minimum Effective Dose (MED) of drugs based on ratio of population means for normally distributed data under heteroscedasticity. The procedure employed Fieller’s (1954) method and obtained individual confidence intervals for identification of MED. The procedure is applied to a data of an experiment that was published by Ruberg (1989) where the effect of a new compound is measured by an increase in the weight of a particular organ in mice. Simulation study was carried out and results indicate that the procedure controls the family-wise error rate (FWER) strongly. Power of the procedure increases with increasing ratio of means and sample size.
异方差下基于正态分布数据比值的最小有效剂量识别
疗效和安全性研究在现代药物开发中具有重要的现实意义。它是评价食品添加剂或农药安全性以及评价药物有效性和安全性的关键组成部分。在大多数不同的统计程序中,要求不同剂量水平之间方差的同质性。本文在不需要多重调整的情况下,对异方差条件下正态分布的数据,提出了一种基于总体均数比的逐步置信集估计药物最小有效剂量的方法。该程序采用了Fieller(1954)的方法,并获得了MED识别的个体置信区间。该程序应用于Ruberg(1989)发表的一项实验数据,该实验通过增加小鼠特定器官的重量来测量新化合物的效果。仿真结果表明,该方法能较好地控制家庭误差率(FWER)。随着均值和样本量的增加,该方法的幂函数也随之增加。
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