The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
James L Kepner - One of the best experts on this subject based on the ideXlab platform.
-
On the maximum Total Sample Size of a group sequential test about bivariate binomial proportions
Statistics & Probability Letters, 2011Co-Authors: James L KepnerAbstract:Abstract For testing “univariate” binomial proportions, it has been proven that, under mild conditions, there exist group sequential designs which satisfy the pre-specified Type I error and power of the single-stage design while the Sample Size is bounded above by that of the single-stage design ( Kepner and Chang, 2003 ). In this article, we extend this result and prove the existence of such group sequential designs for various decision rules in the space of bivariate binomial variables. We also demonstrate how to obtain the actual group sequential designs for detecting changes in bivariate binomial variables.
-
On the maximum Total Sample Size of a group sequential test about binomial proportions
Statistics & Probability Letters, 2003Co-Authors: James L Kepner, Myron ChangAbstract:It is well known that the standard single-stage binomial test is uniformly most powerful to detect an increase or decrease in a binomial proportion. The general perception is that, to achieve a fixed significance level and power, a group sequential test will require a larger maximum Total Sample Size than required by the corresponding standard single-stage test because missing observations are possible under the group sequential test setting. In this article, it is proved that, under mild conditions, there exist group sequential tests which achieve the predesignated significance level and power with maximum Total Sample Size bounded above by the Sample Size for the corresponding standard single-stage test.
Marion K Campbell - One of the best experts on this subject based on the ideXlab platform.
-
Unequal randomisation can improve the economic efficiency of clinical trials.
Journal of health services research & policy, 1997Co-Authors: David J. Torgerson, Marion K CampbellAbstract:Objectives:In the majority of clinical trials patients are randomised equally between treatment groups. This approach maximises statistical power for a given Total Sample Size. The objectives of th...
Myron Chang - One of the best experts on this subject based on the ideXlab platform.
-
On the maximum Total Sample Size of a group sequential test about binomial proportions
Statistics & Probability Letters, 2003Co-Authors: James L Kepner, Myron ChangAbstract:It is well known that the standard single-stage binomial test is uniformly most powerful to detect an increase or decrease in a binomial proportion. The general perception is that, to achieve a fixed significance level and power, a group sequential test will require a larger maximum Total Sample Size than required by the corresponding standard single-stage test because missing observations are possible under the group sequential test setting. In this article, it is proved that, under mild conditions, there exist group sequential tests which achieve the predesignated significance level and power with maximum Total Sample Size bounded above by the Sample Size for the corresponding standard single-stage test.
Julia Singer - One of the best experts on this subject based on the ideXlab platform.
-
A simple procedure to compute the Sample Size needed to compare two independent groups when the population variances are unequal
Statistics in Medicine, 2001Co-Authors: Julia SingerAbstract:The usual methods of Sample Size determination which assume the homogeneity of within-group variances are not sufficiently robust against the violation of this assumption. A method using Satterthwaite's correction for unequal variances is given. It is shown that in the case of unequal variances the minimum Total Sample Size is attained when the allocation ratio is equal to the proportion of the standard deviations.
Wei-ming Luh - One of the best experts on this subject based on the ideXlab platform.
-
Optimum Sample Size allocation to minimize cost or maximize power for the two‐Sample trimmed mean test
British Journal of Mathematical and Statistical Psychology, 2009Co-Authors: Jiin Huarng Guo, Wei-ming LuhAbstract:When planning a study, Sample Size determination is one of the most important tasks facing the researcher. The Size will depend on the purpose of the study, the cost limitations, and the nature of the data. By specifying the standard deviation ratio and/or the Sample Size ratio, the present study considers the problem of heterogeneous variances and non-normality for Yuen's two-group test and develops Sample Size formulas to minimize the Total cost or maximize the power of the test. For a given power, the Sample Size allocation ratio can be manipulated so that the proposed formulas can minimize the Total cost, the Total Sample Size, or the sum of Total Sample Size and Total cost. On the other hand, for a given Total cost, the optimum Sample Size allocation ratio can maximize the statistical power of the test. After the Sample Size is determined, the present simulation applies Yuen's test to the Sample generated, and then the procedure is validated in terms of Type I errors and power. Simulation results show that the proposed formulas can control Type I errors and achieve the desired power under the various conditions specified. Finally, the implications for determining Sample Sizes in experimental studies and future research are discussed.
-
Optimum Sample Size allocation to minimize cost or maximize power for the two-Sample trimmed mean test.
The British journal of mathematical and statistical psychology, 2007Co-Authors: Jiin Huarng Guo, Wei-ming LuhAbstract:When planning a study, Sample Size determination is one of the most important tasks facing the researcher. The Size will depend on the purpose of the study, the cost limitations, and the nature of the data. By specifying the standard deviation ratio and/or the Sample Size ratio, the present study considers the problem of heterogeneous variances and non-normality for Yuen's two-group test and develops Sample Size formulas to minimize the Total cost or maximize the power of the test. For a given power, the Sample Size allocation ratio can be manipulated so that the proposed formulas can minimize the Total cost, the Total Sample Size, or the sum of Total Sample Size and Total cost. On the other hand, for a given Total cost, the optimum Sample Size allocation ratio can maximize the statistical power of the test. After the Sample Size is determined, the present simulation applies Yuen's test to the Sample generated, and then the procedure is validated in terms of Type I errors and power. Simulation results show that the proposed formulas can control Type I errors and achieve the desired power under the various conditions specified. Finally, the implications for determining Sample Sizes in experimental studies and future research are discussed.
