scholarly journals Identifying Minimum Effective Dose Under Heteroscedasticity

2021 ◽  
pp. 34-43
Author(s):  
Michael J. Adjabui ◽  
Jakperik Dioggban ◽  
Irene D. Angbing
Keyword(s):  
Effective Dose ◽  
Simulation Study ◽  
Error Rate ◽  
Familywise Error Rate ◽  
Minimum Effective Dose ◽  
Sample Sizes ◽  
Balanced Design ◽  
Unbalanced Design ◽  
Critical Requirement

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.

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

2021 ◽  
Vol 16 (2) ◽  
pp. 2719-2731
Author(s):  
Emmanuel Dodzi Kpeglo ◽  
Michael Jackson Adjabui ◽  
Jakperik Dioggban
Keyword(s):  
Effective Dose ◽  
Practical Importance ◽  
Food Additives ◽  
Distributed Data ◽  
Familywise Error Rate ◽  
Minimum Effective Dose ◽  
Population Means ◽  
Modern Drug ◽  
Dose Levels ◽  
Normally Distributed

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.

scholarly journals Sample Size Growth with an Increasing Number of Comparisons

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Chi-Hong Tseng ◽  
Yongzhao Shao
Keyword(s):  
Growth Rate ◽  
Sample Size ◽  
Error Rate ◽  
Familywise Error Rate ◽  
Sample Sizes ◽  
False Discovery ◽  
Multiple Hypotheses ◽  
Size Growth ◽  
Study Designs ◽  
Adequate Power

An appropriate sample size is crucial for the success of many studies that involve a large number of comparisons. Sample size formulas for testing multiple hypotheses are provided in this paper. They can be used to determine the sample sizes required to provide adequate power while controlling familywise error rate or false discovery rate, to derive the growth rate of sample size with respect to an increasing number of comparisons or decrease in effect size, and to assess reliability of study designs. It is demonstrated that practical sample sizes can often be achieved even when adjustments for a large number of comparisons are made as in many genomewide studies.

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

scholarly journals The Romano–Wolf multiple-hypothesis correction in Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 812-843
Author(s):  
Damian Clarke ◽  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Error Rate ◽  
Multiple Testing ◽  
Original Data ◽  
Dependence Structure ◽  
Familywise Error Rate ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Multiple Hypothesis ◽  
Wide Range ◽  
Performance Gains

When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

scholarly journals The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Cem Kadilar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Long Memory ◽  
Moving Average ◽  
Mean Square ◽  
Sample Sizes ◽  
Advantages And Disadvantages

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.

The minimum effective dose of praziquantel in treatment of Hymenolepis diminuta in rats

1995 ◽  
Vol 69 (1) ◽  
pp. 91-92 ◽  
Author(s):  
P.C. Fan ◽  
A. Ito
Keyword(s):  
Effective Dose ◽  
Hymenolepis Diminuta ◽  
Minimum Effective Dose ◽  
Faecal Examination ◽  
Faecal Samples

AbstractTo determine the minimum effective dose of praziquantel against Hymenolepis diminuta in rats, 5.0 mg/kg, 2.5 mg/kg, 1.0 mg/kg, 0.5 mg/kg, 0.1 mg/kg, or 0.05 mg/kg praziquantel were given to each of five experimentally infected rats in six groups. Faecal samples from each rat were examined for worms on day 10. Based on the results of faecal examination and autopsy, the minimum effective dose of praziquantel against Hymenolepis diminuta in rats was determined to be 0.5 mg/kg.

scholarly journals A region-based multiple testing method for hypotheses ordered in space or time

2015 ◽  
Vol 14 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Rosa J. Meijer ◽  
Thijmen J.P. Krebs ◽  
Jelle J. Goeman
Keyword(s):  
Error Rate ◽  
Null Hypothesis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Familywise Error Rate ◽  
Expected Number ◽  
Multiple Testing Procedure ◽  
Exact Location ◽  
Testing Method

AbstractWe present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the

Diagnostic and therapeutic applications of radiopharmaceutical agents: The medical use of radioactive iodine (I-131) therapy for hyperthyroidism treatments.

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hasnae WATLA ◽  
Mohamed LAHKIM ◽  
Mohamed Amine CHAD
Keyword(s):  
Nuclear Medicine ◽  
Effective Dose ◽  
Radioactive Iodine ◽  
Minimum Effective Dose ◽  
Therapeutic Aspect ◽  
Therapeutic Applications ◽  
Medical Centers ◽  
Iodine 131 ◽  
Radiopharmaceutical Agents

The treatment of hyperthyroidism with iodine-131 has been recognized on nuclear medicine as simple, effective and inexpensive, this kind of radiopharmaceutical is chosen by the majority of medical centers by administering a minimum effective dose enabling euthyroidism to be easily compensated as quickly as possible while avoiding radiation problems. In this mini_review, we are going to explain the diagnostic and therapeutic aspect of radiopharmaceuticals by taking an example of radioiodine I-131 and its role on hyperthyroidism treatment .

Sign in / Sign up

Export Citation Format

Share Document