Nonparametric Stepwise Procedure for Identification of Maximum Safe Dose (MSD)

Adjabui, Michael Jackson; Howard, Nathaniel; Luguterah, Albert

doi:10.9734/arjom/2017/36377

Cited by 4 publications

(4 citation statements)

References 24 publications

(29 reference statements)

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“…Furthermore, once stepwise confidence intervals are derived, it becomes easy to show that directional errors (that is, correct side decisions at each step) are simultaneously controlled with the familywise error rate (FWER). Adjabui, Howard and Luguterah (2017) proposed a simultaneous nonparametric upper bound procedure based on the Wilcoxon Mann-Whitney test in situations when normal assumptions are violated. Their paper incorporated the partitioning principle for confidence sets procedure proposed by Hsu and Berger (1999) for estimating MSD.…”

Section: Introductionmentioning

confidence: 99%

Identification of Maximum Safe Dose Based on Ratio of Normally Distributed Data under Heteroscedasticity

Kpeglo¹,

Adjabui²,

Dioggban³

2022

JMR

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In most of the various stepwise confidence interval procedures formulated for identifying maximum safe dose (MSD), homogeneity of variances among different dose levels were required. But in practice, homogeneity of variance is often in doubt. This paper proposes a stepwise confidence set procedure for identifying MSD of drugs based on ratio of population means for normally distributed data under heteroscedasticity without the need for multiplicity adjustment. The procedure employed Fieller's method and obtained individual (1-α)100% confidence intervals for identification of the MSD. We illustrate the procedure with a real life example. In addition, we show that power of the procedure increases with increasing ratio of means, and sample size. Power however decreases with increase in clinical relevance margins. We also illustrate that the new procedure can properly control familywise error rate (FWER).

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Section: Introductionmentioning

confidence: 99%

Identification of Maximum Safe Dose Based on Ratio of Normally Distributed Data under Heteroscedasticity

Kpeglo¹,

Adjabui²,

Dioggban³

2022

JMR

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“…However, no multiplicity adjustment is necessary if the experimental groups (new treatments) can be ordered priori according to their treatment effect. Multiple treatments with stepwise procedure without multiplicity has been investigated by these authors but in different settings [8], [9] and [10] are shown to be more powerful than single step procedure. In their papers, they assumed that treatments have the same variances.…”

Section: Introductionmentioning

confidence: 99%

Identifying Minimum Effective Dose Under Heteroscedasticity

Adjabui¹,

Dioggban²,

Angbing³

2021

ARJOM

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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.

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“…Secondly, we simultaneously compare each of the treatments with the negative control. Statistical decision in this settings involves multiple comparison and stepwise procedures: that is, individual inferences are made in stepwise manner if the sequence of individual inferences is in a specific order, as used in Stefensson et al [2], Cao et al [3], Chen [4], and Adjabui et al [5]. Some simultaneous inferences remit multiplicity adjustments by invoking the partition principle proposed by Finner and Strassburger [6]: where the parameter space is partitioned into many disjoint subsets and only one of these nonempty disjoint subsets contains the true parameter of interest, so that the FWER will be properly controlled.…”

Section: Introductionmentioning

confidence: 99%

Biostatistical Assessment of Mutagenicity Studies: A Stepwise Confidence Procedure

Adjabui

Howard

Akamba

2019

Journal of Probability and Statistics

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The paper addresses the issue of identifying the maximum safe dose in the context of noninferiority trials where several doses of toxicological compounds exist. Statistical methodology for identifying the maximum safe dose is available for three-arm noninferiority designs with only one experimental drug treatment. Extension of this methodology for several experimental groups exists but with multiplicity adjustment. However, if the experimental or the treatment groups can be ordered a priori according to their treatment effect, then multiplicity adjustment is unneeded. Assuming homogeneity of variances across dose group in normality settings, we employed the generalized Fieller’s confidence interval method in a multiple comparison stepwise procedure by incorporating the partitioning principle in order to control the familywise error rate (FWER). Simulation results revealed that the procedure properly controlled the FWER in strong sense. Also, the power of our procedure increases with increasing sample size and the ratio of mean differences. We illustrate our procedure with mutagenicity dataset from a clinical study.

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Nonparametric Stepwise Procedure for Identification of Maximum Safe Dose (MSD)

Cited by 4 publications

References 24 publications

Identification of Maximum Safe Dose Based on Ratio of Normally Distributed Data under Heteroscedasticity

Identification of Maximum Safe Dose Based on Ratio of Normally Distributed Data under Heteroscedasticity

Identifying Minimum Effective Dose Under Heteroscedasticity

Biostatistical Assessment of Mutagenicity Studies: A Stepwise Confidence Procedure

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