Identification Of Minimum Effective Dose Research Articles

Identifying Minimum Effective Dose Under Heteroscedasticity

We propose a stepwise confidence procedure for identifying minimum effective dose (MED) without multiplicity adjustment.Stepwise procedures strongly control the familywise error rate (FWER) which is a critical requirement for statistical methodologies in identification of MED. The partitioning principle is invoked to validate the control of the FWER. Our simulation study indicates that the FWER was properly controlled in the case with balanced design but failed in some cases of sample sizes for situations of unbalanced design. In addition, the power of the procedure increases with increasing mean of ratio differences and the sample sizes.

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

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 familywise error rate (FWER) strongly. Power of the procedure increases with increasing ratio of means and sample size.

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

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.

Simultaneous confidence bounds in monotone dose–response studies

In dose–response studies, one of the most important issues is the identification of minimum effective dose (MED), where the MED is defined as the lowest dose such that the mean response is better than the mean response of a zero-dose control by a clinically significant difference. Dose–response curves are sometimes monotonic in nature. In this paper, we propose a new method to construct simultaneous confidence lower bounds for the differences between the mean response of any non-zero dose level and that of the control under the monotonicity assumption to identify the MED. The evaluation of the lower confidence bounds is a concave programming problem subject to homogeneous linear inequality constraints. A necessary and sufficient condition for the solution and its simplified expressions are derived. An efficient computing algorithm is proposed. A real data example from a dose–response study is used to illustrate the method.

Stepwise procedures for the identification of minimum effective dose with unknown variances

Hsu and Berger (J. Amer. Statist. Assoc. 50 (1999) 468) proposed a stepwise confidence interval procedure for identifying the minimum effective dose in dose-response studies under homoscedasticity. In practice, homogeneity of variance is often in doubt. In this paper, we extend Hsu and Berger's stepwise confidence interval procedure to the case of heteroscedasticity (i.e., inequality of variances across dosages). In addition, we show that the new procedure can properly control the family-wise error rate. We illustrate our procedure with a real example. Finally, we investigate the performance of our procedure and a step-down test procedure based on Welch's approximate t-test via a simulation study. Our procedure is shown, empirically, to perform similar to Welch's procedure.