scholarly journals Comparing the "typical score" across independent groups based on different criteria for trimming

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Sharipah Syed Yahaya ◽  
Abdul Othman ◽  
Harvey Keselman
Keyword(s):  
Treatment Group ◽  
Least Squares ◽  
Error Control ◽  
Type I Error ◽  
Bootstrap Method ◽  
Null Distribution ◽  
Type I ◽  
M Estimator ◽  
One Step ◽  
Trimmed Means

Nonnormality and variance heterogeneity affect the validity of the traditional tests for treatment group equality (e.g. ANOVA F-test and t-test), particularly when group sizes are unequal. Adopting trimmed means instead of the usual least squares estimator has been shown to be mostly affective in combating the deleterious effects of nonnormality. There are, however, practical concerns regarding trimmed means, such as the predetermined amount of symmetric trimming that is typically used. Wilcox and Keselman proposed the Modified One- Step M-estimator (MOM) which empirically determines the amount of trimming. Othman et al. found that when this estimator is used with Schrader and Hettmansperger's H statistic, rates of Type I error were well controlled even though data were nonnormal in form. In this paper, we modified the criterion for choosing the sample values for MOM by replacing the default scale estimator, MADn, with two robust scale estimators, Sn and Tn , suggested by Rousseeuw and Croux (1993). To study the robustness of the modified methods, conditions that are known to negatively affect rates of Type I error were manipulated. As well, a bootstrap method was used to generate a better approximate sampling distribution since the null distribution of MOM-H is intractable. These modified methods resulted in better Type I error control especially when data were extremely skewed.

scholarly journals Winsorized Modified One Step M-Estimator As a Measure of the Central Tendency in the Alexander-Govern Test

2016 ◽  
Vol 7 (3) ◽  
pp. 233
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Good Control ◽  
Error Rates ◽  
Extreme Condition ◽  
Type I ◽  
Central Tendency ◽  
Central Tendency Measure ◽  
Type I Error Rates ◽  
M Estimator ◽  
One Step

This research dealt with making comparison of the independent group tests with the use of parametric technique. This test used mean as its central tendency measure. It was a better alternative to the ANOVA, the Welch test and the James test, because it gave a good control of Type I error rates and high power with ease in its calculation, for variance heterogeneity under a normal data. But the test was found not to be robust to non-normal data. Trimmed mean was used on the test as its central tendency measure under non-normality for two group condition, but as the number of groups increased above two, the test failed to give a good control of Type I error rates. As a result of this, the MOM estimator was applied on the test as its central tendency measure and is not influenced by the number of groups. However, under extreme condition of skewness and kurtosis, the MOM estimator could no longer control the Type I error rates. In this study, the Winsorized MOM estimator was used in the AG test, as a measure of its central tendency under non-normality. 5,000 data sets were simulated and analysed for each of the test in the research design with the use of Statistical Analysis Software (SAS) package. The results of the analysis shows that the Winsorized modified one step M-estimator in the Alexander-Govern (AGWMOM) test, gave the best control of Type I error rates under non-normality compared to the AG test, the AGMOM test, and the ANOVA, with the highest number of conditions for both lenient and stringent criteria of robustness. 

scholarly journals The Power of the Test for the Winsorized Modified Alexander-Govern Test

2016 ◽  
Vol 7 (4) ◽  
pp. 307
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Parametric Method ◽  
Type I ◽  
Analysis Software ◽  
Group Case ◽  
Power Of The Test ◽  
M Estimator ◽  
One Step ◽  
Irregular Data ◽  
Statistical Analysis Software

This research examined the usage of the parametric method in comparing two or more means as independent group test, for instance, the Alexander-Govern (AG) test. The utilization of mean as the determinant for the center of distribution of variance diversity takes place in testing, and the test provides excellence in maintaining the amount of Type I error and giving immense sensitivity for a regular data. Unfortunately, it isineffective on irregular data, leading to the application of trimmed mean upon testing as the determinant for the center of distribution under irregular data for two group condition. However, as the group quantity is more than two, the estimator unsuccessfully provides excellence in maintaining the amount of Type I error. Therefore, an estimator high in effectiveness called the MOM estimator was introduced for the testing as the determinant for the center of distribution. Group quantity in a test does not affect the estimator, but it unsuccessfully providesexcellence in maintaining the amount of Type I error under intense asymmetry and unevenness. The application of Winsorized modified one-step M-estimator (WMOM) upon the Alexander-Govern testing takes place so that it can prevail against its drawbacks under irregular data in the presence of variance diversity, can eliminate the presence of the outside observation and can provide effectiveness for the testing on irregular data. Statistical Analysis Software (SAS) was used for the analysis of the tests. The results show that the AGWMOM test gave the most intense sensitivity under g = 0,5 and h = 0,5, for four group case and g = 0 and h = 0, under six group case, differing from three remaining tests and the sensitivity of the AG testing is said suffices and intense enough.

Comparison of Means: An F Test Followed by a Modified Multiple Range Procedure

1979 ◽  
Vol 4 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juliet Popper Shaffer
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Critical Value ◽  
The Other ◽  
Type I ◽  
F Test ◽  
Range Test

If used only when a preliminary F test yields significance, the usual multiple range procedures can be modified to increase the probability of detecting differences without changing the control of Type I error. The modification consists of a reduction in the critical value when comparing the largest and smallest means. Equivalence of modified and unmodified procedures in error control is demonstrated. The modified procedure is also compared with the alternative of using the unmodified range test without a preliminary F test, and it is shown that each has advantages over the other under some circumstances.

scholarly journals Efficient Noninferiority Testing Procedures for Simultaneously Assessing Sensitivity and Specificity of Two Diagnostic Tests

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guogen Shan ◽  
Amei Amei ◽  
Daniel Young
Keyword(s):  
Sensitivity And Specificity ◽  
Diagnostic Tests ◽  
Error Control ◽  
Type I Error ◽  
Binary Outcomes ◽  
Type I ◽  
Test Statistics ◽  
Asymptotic Approach ◽  
Testing Procedures ◽  
Assessing Sensitivity

Sensitivity and specificity are often used to assess the performance of a diagnostic test with binary outcomes. Wald-type test statistics have been proposed for testing sensitivity and specificity individually. In the presence of a gold standard, simultaneous comparison between two diagnostic tests for noninferiority of sensitivity and specificity based on an asymptotic approach has been studied by Chen et al. (2003). However, the asymptotic approach may suffer from unsatisfactory type I error control as observed from many studies, especially in small to medium sample settings. In this paper, we compare three unconditional approaches for simultaneously testing sensitivity and specificity. They are approaches based on estimation, maximization, and a combination of estimation and maximization. Although the estimation approach does not guarantee type I error, it has satisfactory performance with regard to type I error control. The other two unconditional approaches are exact. The approach based on estimation and maximization is generally more powerful than the approach based on maximization.

scholarly journals Statistical inference of genetic pathway analysis in high dimensions

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 651-651
Author(s):  
Yang Liu ◽  
Wei Sun ◽  
Alexander P Reiner ◽  
Charles Kooperberg ◽  
Qianchuan He
Keyword(s):  
Statistical Inference ◽  
Pathway Analysis ◽  
Genetic Variants ◽  
Error Control ◽  
Genome Wide Association Study ◽  
Type I Error ◽  
High Density Lipoproteins ◽  
Type I ◽  
Genetic Pathway ◽  
A Genome

Summary Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size $n$. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension $p$ could be greater than $n$. Our method can be used to detect pathways that have nonsparse weak signals, as well as pathways that have sparse but stronger signals. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has correct Type I error control and is more powerful than existing approaches. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.

scholarly journals A novel gene-set association test based on variance-gamma distribution

2018 ◽  
Vol 28 (9) ◽  
pp. 2868-2875
Author(s):  
Zhongxue Chen ◽  
Qingzhong Liu ◽  
Kai Wang
Keyword(s):  
Gamma Distribution ◽  
Type I Error ◽  
Null Distribution ◽  
Real Data ◽  
Association Test ◽  
P Value ◽  
Type I ◽  
Test Statistic ◽  
Data Set ◽  
Variance Gamma

Several gene- or set-based association tests have been proposed recently in the literature. Powerful statistical approaches are still highly desirable in this area. In this paper we propose a novel statistical association test, which uses information of the burden component and its complement from the genotypes. This new test statistic has a simple null distribution, which is a special and simplified variance-gamma distribution, and its p-value can be easily calculated. Through a comprehensive simulation study, we show that the new test can control type I error rate and has superior detecting power compared with some popular existing methods. We also apply the new approach to a real data set; the results demonstrate that this test is promising.

scholarly journals Type I error control in biomarker-stratified clinical trials

Trials ◽  
2015 ◽  
Vol 16 (S2) ◽  
Author(s):  
Deepak Parashar ◽  
Jack Bowden ◽  
Colin Starr ◽  
Lorenz Wernisch ◽  
Adrian Mander
Keyword(s):  
Clinical Trials ◽  
Error Control ◽  
Type I Error ◽  
Type I

No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Aaron T. L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
Software Package ◽  
Error Control ◽  
Degrees Of Freedom ◽  
Linear Models ◽  
Type I Error ◽  
Real Data ◽  
Type I ◽  
Rna Seq ◽  
Study Gene Expression ◽  
Complex Models

AbstractRNA sequencing (RNA-seq) is widely used to study gene expression changes associated with treatments or biological conditions. Many popular methods for detecting differential expression (DE) from RNA-seq data use generalized linear models (GLMs) fitted to the read counts across independent replicate samples for each gene. This article shows that the standard formula for the residual degrees of freedom (d.f.) in a linear model is overstated when the model contains fitted values that are exactly zero. Such fitted values occur whenever all the counts in a treatment group are zero as well as in more complex models such as those involving paired comparisons. This misspecification results in underestimation of the genewise variances and loss of type I error control. This article proposes a formula for the reduced residual d.f. that restores error control in simulated RNA-seq data and improves detection of DE genes in a real data analysis. The new approach is implemented in the quasi-likelihood framework of the edgeR software package. The results of this article also apply to RNA-seq analyses that apply linear models to log-transformed counts, such as those in the limma software package, and more generally to any count-based GLM where exactly zero fitted values are possible.

Association of human endogenous retrovirus (hERV) expression with clinical efficacy of PD-1 blockade in metastatic clear cell renal cell carcinoma (mccRCC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4568-4568 ◽  
Author(s):  
Jean-Christophe Pignon ◽  
Opeyemi Jegede ◽  
Sachet A Shukla ◽  
David A. Braun ◽  
Christine Horak ◽  
...  
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Endogenous Retrovirus ◽  
Phase Iii ◽  
Human Endogenous Retrovirus ◽  
Rank Test ◽  
Type I ◽  
Limit Of Quantification ◽  
Cell Renal Cell Carcinoma ◽  
Exact Test

4568 Background: hERV levels positively correlate with tumor immune infiltrate and were recently shown to be associated with clinical benefit to PD-1/PD-L1 blockade in two small cohorts of patients (pts) with mccRCC (Smith C.C. et al and Panda A. et al; 2018). We tested whether hERV levels correlate with efficacy of nivolumab in a prospective phase II study of pts with mccRCC (Checkmate 010). Methods: Reverse transcribed RNA extracted from 99 FFPE pretreatment tumors were analyzed by RT-qPCR to assess levels of pan- ERVE4, pan- ERV3.2, hERV4700 GAG or ENV, and the reference genes 18S and HPRT1. Normalized hERV levels were transformed as categorical value (high or low) using population quartiles as cutoffs. For each cutoff, samples with non-quantifiable hERV levels for which the limit of quantification was above the tested cutoff could not be categorized and were excluded from analysis. Log rank test was used to test the association of hERV levels with PFS/irPFS (RECISTv1.1/irRECIST) at each cutoff using Holm-Bonferroni correction for Type I error control; adjusted P-values are reported. Fisher’s exact test was then used to explore the association with ORR/irORR (RECISTv1.1/irRECIST). Results: Among the hERV studied, only hERV4700 ENV was significantly associated with PFS/irPFS. At the 25th percentile cutoff, 45 pts had high levels of hERV4700 ENV and 24 pts had low levels of hERV4700 ENV. Median PFS and irPFS were significantly longer in the high- hERV4700 ENV group [7.0 (95% CI: 2.2 - 10.2) and 8.5 (95% CI: 4.2 - 14.1) months, respectively] versus the low- hERV4700 ENV group [2.6 (95% CI: 1.4 - 5.4) and 2.9 (95% CI: 1.4 - 5.7) months, respectively] ( P = 0.010 for PFS and P = 0.028 for irPFS). At the same cutoff, ORR and irORR rates were significantly higher in the high- hERV4700 ENV group [35.6 (95% CI: 21.9 - 51.2) % for both ORR/irORR] versus the low- hERV4700 ENV group [12.5 (95% CI: 2.7 - 32.4) and 8.3 (95% CI: 1.0 - 27.0) %, respectively] ( P = 0.036 for ORR and P = 0.012 for irORR). Conclusions: hERV4700 ENV levels may predict outcome on nivolumab in mccRCC. Validation of our results and correlation of hERV levels with immune markers in a controlled phase III trial (CheckMate 025) is ongoing.

scholarly journals Comparing Means under Heteroscedasticity and Nonnormality: Further Exploring Robust Means Modeling

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Alyssa Counsell ◽  
Robert Philip Chalmers ◽  
Robert A. Cribbie
Keyword(s):  
Monte Carlo ◽  
Error Control ◽  
Type I Error ◽  
Monte Carlo Study ◽  
Type I ◽  
Variance Heterogeneity ◽  
Variance Homogeneity

Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM.

Sign in / Sign up

Export Citation Format

Share Document