The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Patricia P Ramsey - One of the best experts on this subject based on the ideXlab platform.
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power of pairwise comparisons in the equal variance and Unequal Sample Size case
British Journal of Mathematical and Statistical Psychology, 2008Co-Authors: Philip H Ramsey, Patricia P RamseyAbstract:A Monte Carlo simulation was conducted to compare five, pairwise multiple comparison procedures. The number of means varied from 4 to 6 and the Sample Size ratio varied from 1 to 60. Procedures were evaluated on the basis of Type I errors, any-pair power and all-pairs power. Four procedures were shown to be conservative, while the fifth provided adequate control of Type I errors only for restricted values of Sample Size ratios. No procedure was found to be uniformly most powerful. The Tukey-Kramer procedure was found to provide the best any-pair power provided it is applied without requiring a significant overall F test. In most cases, the Hayter-Fisher modification of the Tukey-Kramer was found to provide very good any-pair power and to be uniformly more powerful than the Tukey-Kramer when a significant overall F test is required. A partition-based version of Peritz's method usually provided the greatest all-pairs power. A modification of the Shaffer-Welsch was found to be useful in certain conditions.
Philip H Ramsey - One of the best experts on this subject based on the ideXlab platform.
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power of pairwise comparisons in the equal variance and Unequal Sample Size case
British Journal of Mathematical and Statistical Psychology, 2008Co-Authors: Philip H Ramsey, Patricia P RamseyAbstract:A Monte Carlo simulation was conducted to compare five, pairwise multiple comparison procedures. The number of means varied from 4 to 6 and the Sample Size ratio varied from 1 to 60. Procedures were evaluated on the basis of Type I errors, any-pair power and all-pairs power. Four procedures were shown to be conservative, while the fifth provided adequate control of Type I errors only for restricted values of Sample Size ratios. No procedure was found to be uniformly most powerful. The Tukey-Kramer procedure was found to provide the best any-pair power provided it is applied without requiring a significant overall F test. In most cases, the Hayter-Fisher modification of the Tukey-Kramer was found to provide very good any-pair power and to be uniformly more powerful than the Tukey-Kramer when a significant overall F test is required. A partition-based version of Peritz's method usually provided the greatest all-pairs power. A modification of the Shaffer-Welsch was found to be useful in certain conditions.
Pierreantoine Gourraud - One of the best experts on this subject based on the ideXlab platform.
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comparison of high resolution human leukocyte antigen haplotype frequencies in different ethnic groups consequences of sampling fluctuation and haplotype frequency distribution tail truncation
Human Immunology, 2015Co-Authors: Derek J Pappas, Alannah Tomich, Federico Garnier, Evelyne Marry, Pierreantoine GourraudAbstract:Abstract High-resolution haplotype frequency estimations and descriptive metrics are becoming increasingly popular for accurately describing human leukocyte antigen diversity. In this study, we compared Sample sets of publically available haplotype frequencies from different populations to characterize the consequences of Unequal Sample Size on haplotype frequency estimation. We found that for low Samples Sizes (a few thousand), haplotype frequencies were overestimated, affecting all descriptive metrics of the underlying distribution, such as most frequent haplotype, the number of haplotypes, and the mean/median frequency. This overestimation was a result of random Sample fluctuation and truncation of the tail end of the frequency distribution that comprises the least frequent haplotypes. Finally, we simulated balanced datasets through resampling and contrasted the disparities of descriptive metrics among equal and Unequal datasets. This simulation resulted in the global description of the most frequent human leukocyte antigen haplotypes worldwide.
Derek J Pappas - One of the best experts on this subject based on the ideXlab platform.
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comparison of high resolution human leukocyte antigen haplotype frequencies in different ethnic groups consequences of sampling fluctuation and haplotype frequency distribution tail truncation
Human Immunology, 2015Co-Authors: Derek J Pappas, Alannah Tomich, Federico Garnier, Evelyne Marry, Pierreantoine GourraudAbstract:Abstract High-resolution haplotype frequency estimations and descriptive metrics are becoming increasingly popular for accurately describing human leukocyte antigen diversity. In this study, we compared Sample sets of publically available haplotype frequencies from different populations to characterize the consequences of Unequal Sample Size on haplotype frequency estimation. We found that for low Samples Sizes (a few thousand), haplotype frequencies were overestimated, affecting all descriptive metrics of the underlying distribution, such as most frequent haplotype, the number of haplotypes, and the mean/median frequency. This overestimation was a result of random Sample fluctuation and truncation of the tail end of the frequency distribution that comprises the least frequent haplotypes. Finally, we simulated balanced datasets through resampling and contrasted the disparities of descriptive metrics among equal and Unequal datasets. This simulation resulted in the global description of the most frequent human leukocyte antigen haplotypes worldwide.
John R Feussner - One of the best experts on this subject based on the ideXlab platform.
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an overview of variance inflation factors for Sample Size calculation
Evaluation & the Health Professions, 2003Co-Authors: Frank Y Hsieh, Philip W Lavori, Harvey J Cohen, John R FeussnerAbstract:For power and Sample-Size calculations, most practicing researchers rely on power and Sample-Size software programs to design their studies. There are many factors that affect the statistical power that, in many situations, go beyond the coverage of commercial software programs. Factors commonly known as design effects influence statistical power by inflating the variance of the test statistics. The authors quantify how these factors affect the variances so that researchers can adjust the statistical power or Sample Size accordingly. The authors review design effects for factorial design, crossover design, cluster randomization, Unequal Sample-Size design, multiarm design, logistic regression, Cox regression, and the linear mixed model, as well as missing data in various designs. To design a study, researchers can apply these design effects, also known as variance inflation factors to adjust the power or Sample Size calculated from a two-group parallel design using standard formulas and software.
