Using statistical equivalence testing in clinical biofeedback research

1996 ◽  
Vol 21 (2) ◽  
pp. 105-119 ◽  
Author(s):  
John P. Hatch
Keyword(s):  
Equivalence Testing ◽  
Statistical Equivalence ◽  
Clinical Biofeedback ◽  
Biofeedback Research

Comparing Intensive English Program (IEP) and Directly-Admitted International Students’ Academic Achievement

2020 ◽  
Author(s):  
Anthony Schmidt
Keyword(s):  
Higher Education ◽  
International Students ◽  
Equivalence Testing ◽  
Intensive English ◽  
Intensive English Program ◽  
Student Groups ◽  
First Semester ◽  
Intensive English Programs ◽  
Statistical Equivalence ◽  
Direct Admission

Intensive English programs (IEPs) exist as an additional pathway into higher education for international students who need additional language support before full matriculation. Despite their long history in higher education, there is little research on the effectiveness of these programs. The current research examines the effectiveness of an IEP by comparing IEP students to directly-admitted international students. Results from regression models on first-semester and first-year GPA indicated no significant differences between these two student groups. Follow-up equivalence testing indicated statistical equivalence in several cases. The findings lead to the conclusion that the IEP is effective in helping students perform on par with directly-admitted international students. These findings imply further support for IEPs and alterative pathways to direct admission.

scholarly journals Beyond the t-Test: Statistical Equivalence Testing

2005 ◽  
Vol 77 (11) ◽  
pp. 221 A-226 A ◽  
Author(s):  
Giselle B. Limentani ◽  
Moira C. Ringo ◽  
Feng Ye ◽  
Mandy L. Bergquist ◽  
Ellen O. MCSorley
Keyword(s):  
T Test ◽  
Equivalence Testing ◽  
Statistical Equivalence

Statistical Equivalence Testing Approaches for Mantel–Haenszel DIF Analysis

2017 ◽  
Vol 43 (4) ◽  
pp. 407-439 ◽  
Author(s):  
Jodi M. Casabianca ◽  
Charles Lewis
Keyword(s):  
Null Hypothesis ◽  
Empirical Bayes ◽  
International Student ◽  
Hypothesis Test ◽  
Student Assessment ◽  
Equivalence Testing ◽  
Test Form ◽  
International Student Assessment ◽  
Statistical Equivalence ◽  
Alternative Hypotheses

The null hypothesis test used in differential item functioning (DIF) detection tests for a subgroup difference in item-level performance—if the null hypothesis of “no DIF” is rejected, the item is flagged for DIF. Conversely, an item is kept in the test form if there is insufficient evidence of DIF. We present frequentist and empirical Bayes approaches for implementing statistical equivalence testing for DIF using the Mantel–Haenszel (MH) DIF statistic. With these approaches, rejection of the null hypothesis of “DIF” allows the conclusion of statistical equivalence, a more stringent criterion for keeping items. In other words, the roles of the null and alternative hypotheses are interchanged in order to have positive evidence that the DIF of an item is small. A simulation study compares the equivalence testing approaches to the traditional MH DIF detection method with the Educational Testing Service classification system. We illustrate the methods with item response data from the 2012 Programme for International Student Assessment.

scholarly journals Pooling morphometric estimates: a statistical equivalence approach

2015 ◽  
Author(s):  
Heath R Pardoe ◽  
Gary Cutter ◽  
Rachel A Alter ◽  
Rebecca Kucharsky Hiess ◽  
Mira Semmelroch ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Cortical Thickness ◽  
Power Analysis ◽  
Hippocampal Volume ◽  
Equivalence Testing ◽  
Volume Data ◽  
Experimental Conditions ◽  
Automated Method ◽  
Statistical Equivalence ◽  
Volume Estimates

Changes in hardware or image processing settings are a common issue for large multi-center studies. In order to pool MRI data acquired under these changed conditions, it is necessary to demonstrate that the changes do not affect MRI-based measurements. In these circumstances classical inference testing is inappropriate because it is designed to detect differences, not prove similarity. We used a method known as statistical equivalence testing to address this limitation. Equivalence testing was carried out on three datasets: (i) cortical thickness and automated hippocampal volume estimates obtained from healthy individuals imaged using different multi-channel head coils; (ii) manual hippocampal volumetry obtained using two readers; and (iii) corpus callosum area estimates obtained using an automated method with manual cleanup carried out by two readers. Equivalence testing was carried out using the “two one-sided tests” (TOST) approach. Power analyses of the two one-sided tests were used to estimate sample sizes required for well-powered equivalence testing analyses. Mean and standard deviation estimates from the automated hippocampal volume dataset were used to carry out an example power analysis. Cortical thickness values were found to be equivalent over 61% of the cortex when different head coils were used (q < 0.05, FDR correction). Automated hippocampal volume estimates obtained using the same two coils were statistically equivalent (TOST p = 4.28 × 10-15). Manual hippocampal volume estimates obtained using two readers were not statistically equivalent (TOST p = 0.97). The use of different readers to carry out limited correction of automated corpus callosum segmentations yielded equivalent area estimates (TOST p = 1.28 × 10-14). Power analysis of simulated and automated hippocampal volume data demonstrated that the equivalence margin affects the number of subjects required for well-powered equivalence tests. We have presented a statistical method for determining if morphometric measures obtained under variable conditions can be pooled. The equivalence testing technique is applicable for analyses in which experimental conditions vary over the course of the study.

Testing for additivity in chemical mixtures using a fixed-ratio ray design and statistical equivalence testing methods

2007 ◽  
Vol 12 (4) ◽  
pp. 514-533 ◽  
Author(s):  
LeAnna G. Stork ◽  
Chris Gennings ◽  
Walter H. Carter ◽  
Robert E. Johnson ◽  
Darcy P. Mays ◽  
...  
Keyword(s):  
Fixed Ratio ◽  
Equivalence Testing ◽  
Testing Methods ◽  
Chemical Mixtures ◽  
Statistical Equivalence

scholarly journals Establishing Statistical Equivalence of Data from Different Sampling Approaches for Assessment of Bacterial Phenotypic Antimicrobial Resistance

2018 ◽  
Vol 84 (9) ◽  
Author(s):  
Heman Shakeri ◽  
Victoriya Volkova ◽  
Xuesong Wen ◽  
Andrea Deters ◽  
Charley Cull ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Antimicrobial Susceptibility ◽  
Antimicrobial Drug ◽  
Equivalence Testing ◽  
Sample Type ◽  
Sample Processing ◽  
Processing Methods ◽  
Content Type ◽  
Statistical Equivalence ◽  
The Difference

ABSTRACTTo assess phenotypic bacterial antimicrobial resistance (AMR) in different strata (e.g., host populations, environmental areas, manure, or sewage effluents) for epidemiological purposes, isolates of target bacteria can be obtained from a stratum using various sample types. Also, different sample processing methods can be applied. The MIC of each target antimicrobial drug for each isolate is measured. Statistical equivalence testing of the MIC data for the isolates allows evaluation of whether different sample types or sample processing methods yield equivalent estimates of the bacterial antimicrobial susceptibility in the stratum. We demonstrate this approach on the antimicrobial susceptibility estimates for (i) nontyphoidalSalmonellaspp. from ground or trimmed meat versus cecal content samples of cattle in processing plants in 2013-2014 and (ii) nontyphoidalSalmonellaspp. from urine, fecal, and blood human samples in 2015 (U.S. National Antimicrobial Resistance Monitoring System data). We found that the sample types for cattle yielded nonequivalent susceptibility estimates for several antimicrobial drug classes and thus may gauge distinct subpopulations of salmonellae. The quinolone and fluoroquinolone susceptibility estimates for nontyphoidal salmonellae from human blood are nonequivalent to those from urine or feces, conjecturally due to the fluoroquinolone (ciprofloxacin) use to treat infections caused by nontyphoidal salmonellae. We also demonstrate statistical equivalence testing for comparing sample processing methods for fecal samples (culturing one versus multiple aliquots per sample) to assess AMR in fecalEscherichia coli. These methods yield equivalent results, except for tetracyclines. Importantly, statistical equivalence testing provides the MIC difference at which the data from two sample types or sample processing methods differ statistically. Data users (e.g., microbiologists and epidemiologists) may then interpret practical relevance of the difference.IMPORTANCEBacterial antimicrobial resistance (AMR) needs to be assessed in different populations or strata for the purposes of surveillance and determination of the efficacy of interventions to halt AMR dissemination. To assess phenotypic antimicrobial susceptibility, isolates of target bacteria can be obtained from a stratum using different sample types or employing different sample processing methods in the laboratory. The MIC of each target antimicrobial drug for each of the isolates is measured, yielding the MIC distribution across the isolates from each sample type or sample processing method. We describe statistical equivalence testing for the MIC data for evaluating whether two sample types or sample processing methods yield equivalent estimates of the bacterial phenotypic antimicrobial susceptibility in the stratum. This includes estimating the MIC difference at which the data from the two approaches differ statistically. Data users (e.g., microbiologists, epidemiologists, and public health professionals) can then interpret whether that present difference is practically relevant.

scholarly journals Pooling morphometric estimates: a statistical equivalence approach

2015 ◽  
Author(s):  
Heath R Pardoe ◽  
Gary Cutter ◽  
Rachel A Alter ◽  
Rebecca Kucharsky Hiess ◽  
Mira Semmelroch ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Cortical Thickness ◽  
Hippocampal Volume ◽  
Equivalence Testing ◽  
Experimental Conditions ◽  
Automated Method ◽  
Statistical Equivalence ◽  
Equivalent Area ◽  
Classical Inference ◽  
Volume Estimates

Changes in hardware or image processing settings are a common issue for large multi-center studies. In order to pool MRI data acquired under these changed conditions, it is necessary to demonstrate that the changes do not affect MRI-based measurements. In these circumstances classical inference testing is inappropriate because it is designed to detect differences, not prove similarity. We used a method known as statistical equivalence testing to address this limitation. Equivalence testing was carried out on three datasets: (i) cortical thickness and automated hippocampal volume estimates obtained from 16 healthy individuals imaged different multi-channel head coils; (ii) manual hippocampal volumetry obtained using two readers; and (iii) corpus callosum area estimates obtained using an automated method with manual cleanup carried out by two readers. Equivalence testing was carried out using the “two one-sided tests” approach. Cortical thickness values were found to be equivalent over 78% of the cortex when different head coils were used (p = 0.024). Automated hippocampal volume estimates obtained using the same two coils were statistically equivalent (p = 4.28 × 10-15). Manual hippocampal volume estimates obtained using two readers were not statistically equivalent (p = 0.97). The use of different readers to carry out limited correction of automated corpus callosum segmentations yielded equivalent area estimates (1.28 × 10-14). We have presented a statistical method for determining if morphometric measures obtained under variable conditions can be pooled. The equivalence testing technique is applicable for analyses in which experimental conditions vary over the course of the study.

scholarly journals Pooling morphometric estimates: a statistical equivalence approach

2015 ◽  
Author(s):  
Heath R Pardoe ◽  
Gary Cutter ◽  
Rachel A Alter ◽  
Rebecca Kucharsky Hiess ◽  
Mira Semmelroch ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Cortical Thickness ◽  
Power Analysis ◽  
Hippocampal Volume ◽  
Equivalence Testing ◽  
Volume Data ◽  
Experimental Conditions ◽  
Automated Method ◽  
Statistical Equivalence ◽  
Volume Estimates

Changes in hardware or image processing settings are a common issue for large multi-center studies. In order to pool MRI data acquired under these changed conditions, it is necessary to demonstrate that the changes do not affect MRI-based measurements. In these circumstances classical inference testing is inappropriate because it is designed to detect differences, not prove similarity. We used a method known as statistical equivalence testing to address this limitation. Equivalence testing was carried out on three datasets: (i) cortical thickness and automated hippocampal volume estimates obtained from healthy individuals imaged using different multi-channel head coils; (ii) manual hippocampal volumetry obtained using two readers; and (iii) corpus callosum area estimates obtained using an automated method with manual cleanup carried out by two readers. Equivalence testing was carried out using the “two one-sided tests” (TOST) approach. Power analyses of the two one-sided tests were used to estimate sample sizes required for well-powered equivalence testing analyses. Mean and standard deviation estimates from the automated hippocampal volume dataset were used to carry out an example power analysis. Cortical thickness values were found to be equivalent over 61% of the cortex when different head coils were used (q < 0.05, FDR correction). Automated hippocampal volume estimates obtained using the same two coils were statistically equivalent (TOST p = 4.28 × 10-15). Manual hippocampal volume estimates obtained using two readers were not statistically equivalent (TOST p = 0.97). The use of different readers to carry out limited correction of automated corpus callosum segmentations yielded equivalent area estimates (TOST p = 1.28 × 10-14). Power analysis of simulated and automated hippocampal volume data demonstrated that the equivalence margin affects the number of subjects required for well-powered equivalence tests. We have presented a statistical method for determining if morphometric measures obtained under variable conditions can be pooled. The equivalence testing technique is applicable for analyses in which experimental conditions vary over the course of the study.

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