The DOI matches its Crossref record.
Testing Statistical Hypotheses of Equivalence and Noninferiority
Abstract
While continuing to focus on methods of testing for two-sided equivalence, Testing Statistical Hypotheses of Equivalence and Noninferiority, Second Edition gives much more attention to noninferiority testing. It covers a spectrum of equivalence testing problems of both types, ranging from a one-sample problem with normally distributed observations
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The non-inferiority margin was defined a priori (in the grant application) by a strict and small margin of tolerance for non-inferiority of 10%, or a ε of 0.36 [53], p.16 which, for example, represents a difference in change between the two conditions of no more than 10% on the primary outcome measure.”
In the citing paper
“The sample size was established a priori based on a power analysis for non-inferiority trials [53,54].”
Citations
Cited by
Sorted by citations
Items 401–500 of 553
Impact of anatomical variations in ventricular shape on non-invasive electrocardiographic imaging
Rahimi, A., Mao, H., Wong, K. C. L., et al. (2012) · Cited by 1
The impact of ventricular shape variations on inverse electrocardiography: A feasibility study
Rahimi, A., & Wang, L. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556537 · Cited by 1
“5 [8], with 40 number of pairs, dθ resides in the rejection region at α level = 5%.”
BackgroundMultigroup Equivalence Analysis for High-Dimensional Expression Data
Yang, C. T., Bartolucci, A. A., & Cui, X. (2015) · Cancer Informatics · doi:10.4137/cin.s17304 · Cited by 1
Wellek, S. (2015) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2015.1081906 · Cited by 1
Self-citationWellek, S. (2017) · Statistics in Medicine · doi:10.1002/sim.7360 · Cited by 1
“The confirmatory inferential procedure to be eventually carried out consists of three two-arm comparisons between RP and one alternative therapy by means of the logrank test for noninferiority [2,10].”
MethodSelf-citationCao, C., Kim, E., Chen, Y., & Ferron, J. M. (2021) · Educational and Psychological Measurement · doi:10.1177/0013164421992407 · Cited by 1
“Equivalence testing allows the researchers to control the degree of model misspecification (Wellek, 2010).”
BackgroundStatistically Significant versus Practically Relevant Trend in Stability Data
Schmelzer, B., Mischo, A., & Innerbichler, F. (2021) · PDA Journal of Pharmaceutical Science and Technology · doi:10.5731/pdajpst.2020.012336 · Cited by 1
Vollrath, M. (2015) · Springer eBooks · doi:10.1007/978-3-8348-2297-0_12 · Cited by 1
Rother, K. M., Karl, I., & Nestler, S. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5469-1.ch008 · Cited by 1
Tests of equality and equivalence of regression slopes: R Shiny apps for optimal sample sizes
Luh, W. (2022) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2022.2103566 · Cited by 1
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.014 · Cited by 1
An Rkhs Approach for Pivotal Inference in Functional Linear Regression
Dette, H., & Tang, J. (2022) · Statistica Sinica · doi:10.5705/ss.202022.0086 · Cited by 1
“We refer to Hodges and Lehmann (1954); Berger and Delampady (1987) for a theoretical discussion of relevant hypotheses, and Chow and Liu (1992); Wellek (2010) for applications in biostatistics, where these references concentrate on real valued (or finite dimensional) parameters.”
BackgroundDufera, A. G., Xiong, C., & Xu, J. (2023) · Biometrical Journal · doi:10.1002/bimj.202200082 · Cited by 1
“It is noted that like the extended TOST method, this IUT test can be conservative too with reduced type-I error below the nominal size (Wellek, 2010, Section 8.2.3).”
MethodInfluentialTesting for signal-to-noise ratio in linear regression: a test under large or massive sample
Kim, J. H., & Ji, P. I. (2023) · Reviews of Management Sciences · doi:10.1007/s11846-023-00706-0 · Cited by 2
Problems with Constructing Tests to Accept the Null Hypothesis
Rogatko, A., & Piantadosi, S. (2015) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-12454-4_4 · Cited by 1
Harel, M., & Mannor, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2404836 · Cited by 1
“For the second test, we apply the principle of CI inclusion [23], which states that if 1⁄2CI;CI 1⁄20; g , dissimilarity is rejected and similarity deduced.”
MethodOn Detecting a Minimal Important Difference among Standardized Means
Shieh, G. (2016) · Current Psychology · doi:10.1007/s12144-016-9549-5 · Cited by 1
New Equivalence Tests for Approximate Independence in Contingency Tables
Ostrovski, V. (2019) · Stats · doi:10.3390/stats2020018 · Cited by 1
“[Wellek(2010)] develops a test for independence in multi-way contingency tables in Section 9.2.”
InfluentialMelting pot kindergarten: The effect of linguistic diversity in early education
Jürges, H., Makles, A., Naghavi, A., & Schneider, K. (2022) · Labour Economics · doi:10.1016/j.labeco.2022.102119 · Cited by 1
Sandie, A. B., Molinari, N., Wanjoya, A., Kouanfack, C., Laurent, C., & Mbougua, J. B. T. (2022) · Trials · doi:10.1186/s13063-022-06118-x · Cited by 1
“For the non-inferiority test with continuous endpoints and fixed margin, some studies like [7] and [12] studied the confidence interval approach which does not allowed for explicit sample size calculation.”
BackgroundInfluentialPhillips, P. P. J., & Glidden, D. V. (2021) · Springer eBooks · doi:10.1007/978-3-319-52677-5_98-1 · Cited by 2
Using a baseline with the probability of agreement to compare distribution characteristics
Lu, L., Anderson‐Cook, C. M., Stevens, N. T., et al. (2022) · Quality Engineering · doi:10.1080/08982112.2022.2073452 · Cited by 1
“Alternative methods, such as non-parametric hypothesis tests (Gibbons and Chakraborti 2020), equivalence tests (Wellek 2010; Anderson-Cook and Borror 2016; Richter and Richter 2002), and the probability of agreement (Stevens, Steiner, and MacKay 2017; Stevens, Rigdon, and Anderson-Cook 2018a) all…”
MethodAre Experts Well-Calibrated? An Equivalence-Based Hypothesis Test
Dharmarathne, G., Hanea, A. M., & Robinson, A. P. (2022) · Entropy · doi:10.3390/e24060757 · Cited by 1
“, [13]) where the rejection region is collapsed to the single value Pr · n.”
BackgroundMethodPhillips, P. P. J., & Glidden, D. V. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_98 · Cited by 1
Sensitivity Analyses for Informative Censoring in Time-to-Event Clinical Trials
Fink, S. (2015) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.5282/ubm/epub.25582 · Cited by 1
“The theory for the TOST is taken from [26] and [32].”
MethodBayesian Hypothesis Assessment in Two-arm Trials Using Relative Belief RatiosPreprint
Muthukumarana, S., & Evans, M. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.4215 · Cited by 1
“This kind of clinical trial is known as an equivalence trial [2].”
MethodResultThe Bayesian New Statistics (Public)Preprint
Kruschke, J. K., & Liddell, T. M. (2017) · doi:10.31234/osf.io/wntsa · Cited by 1
G-TwYST harmonisation of statistical methods for use of omics data in food safety assessment
Engel, J., & van der Voet, H. (2018) · doi:10.18174/455159 · Cited by 1
PD-ML-Lite: Private Distributed Machine Learning from Lighweight CryptographyPreprint
Tsikhanovich, M., Magdon‐Ismail, M., Ishaq, M. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07986 · Cited by 1
Padilla, M. A. (2019) · Springer eBooks · doi:10.1007/978-3-030-26837-4_30 · Cited by 1
Equivalence testing for standardized effect sizes in linear regressionPreprint
Campbell, H. (2020) · arXiv (Cornell University) · Cited by 1
Chen, L.-C., & Wang, J. (2020) · Biometrics · doi:10.1111/biom.13255 · Cited by 1
“The goals of an equivalence test in model fitting are mainly to control the size of misspecification and to support evidence of the goodness-of-fit for a model (Wellek, 2010).”
BackgroundAmbient Temperature, Social Perception and Social BehaviorPreprint
Krause, J., Nanninga, G., Ring, P., Schmidt, U., & Schunk, D. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3583038 · Cited by 1
Synthetic Area Weighting for Measuring Public Opinion in Small AreasPreprint
Kuriwaki, S., & Yamauchi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05829 · Cited by 1
Selection Performance Using a Smartphone in VR with Redirected Input
Kyian, S. (2021) · doi:10.22215/etd/2021-14777 · Cited by 1
A Step-By-Step Guide on Preregistration and Effective Data Sharing for Psychopathology ResearchPreprint
Krypotos, A., Klugkist, I. G., Mertens, G., et al. (2018) · doi:10.31234/osf.io/ysgfa · Cited by 1
Tackling False Positives in Finance: A Statistical Toolbox with ApplicationsPreprint
Kim, J. H. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3217003 · Cited by 1
Effect Sizes for Equivalence Testing: Incorporating the Equivalence IntervalPreprint
Gutierrez, N. M., & Cribbie, R. A. (2022) · doi:10.31234/osf.io/5buz9 · Cited by 1
Replication of “null results” – Absence of evidence or evidence of absence?Preprint
Pawel, S., Heyard, R., Micheloud, C., & Held, L. (2023) · eLife · doi:10.7554/elife.92311.1 · Cited by 1
“Equivalence testing was developed in the context of clinical trials to assess whether a new treat-ment – typically cheaper or with fewer side effects than the established treatment – is practically equivalent to the established treatment ( Wellek, 2010 ; Lakens, 2017 ).”
BackgroundMethodT2EQ: Functions for Applying the T^2-Test for Equivalence
Hoffelder, T. (2016) · doi:10.32614/cran.package.t2eq · Cited by 1
Establishing Practical Equivalence Between Three Treatments
Lin, Y., Hayter, A. J., & Liu, W. (2014) · Journal of Statistical Theory and Practice · doi:10.1080/15598608.2014.971985
Characterizing Crown Biomass and Crown Profi les in Conifer Forests of the Interior Northwest
Affleck, D. L. R., Seielstad, C., Goodburn, J. M., et al. (2013) · Lincoln (University of Nebraska)
The Effects of Mental Models and Expertise on Running Memory and Clinical Handoff Effectiveness
Anderson-Montoya, B. L. (2015) · ODU Digital Commons (Old Dominion University) · doi:10.25777/vwyc-9h69
“Regarding expertise level, confidence interval equivalence tests (Snow, Reising, Barry, & Hartstock, 1999; Wellek, 2010) were performed to determine whether the performance of the novices and intermediates was equivalent to the experts for each type of response.”
MethodCrain, D. R. (2015) · UA Campus Repository (The University of Arizona)
An approach for quantifying small effects in regression models
Bedrick, E. J., & Hund, L. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216653152
A RANK-BASED PERMUTATION TEST FOR EQUIVALENCE AND NON-INFERIORITY
Arboretti, R., Carrozzo, E., & Caughey, D. (2015)
Essays on the Role and Function of Political Parties
Rainey, R. C. (2013)
Hinnah, F. D. (2018) · doi:10.11606/t.11.2018.tde-30072018-154610
“Equivalence tests – the method we used - reverse the usual null and alternative hypotheses (McBride 2005; Wellek 2010).”
MethodSchmidtmann, I. M., Konstantinides, S., & Binder, H. (2018) · Biometrical Journal · doi:10.1002/bimj.201700296
“A Mann–Whitney test for equivalence is, for example, described by Wellek (2010); the noninferiority test is the special case in which one equivalence boundary is set to infinity.”
BackgroundMethodMaennig, W., Sattarhoff, C., & Stahlecker, P. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3063233
Bagheri, E. (2019) · doi:10.32657/10220/48049
“The term non-inferiority [190] is used to denote the absence of a statistically significant difference in performance between the human readers and the automated detector.”
MethodCampbell, H., & Lakens, D. (2019) · arXiv (Cornell University)
Phonological Activeness Bias Effects in Language Acquisition and Language Structuring
Carter, W. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/qja5-ky56
Mielke, J., & Jones, B. C. (2020) · Springer eBooks · doi:10.1007/978-3-319-52677-5_272-1
Zhong, J., Wen, M.-J., Cheung, S. H., & Poon, W. (2020) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2020.1747082
The Myth of “The Myth of Objectivity”
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.009
Efficient tests for bio-equivalence in functional dataPreprint
Dette, H., & Kokot, K. (2020) · arXiv (Cornell University) · doi:10.17877/de290r-21015
A comparative study of TOST and UMPT procedures for evaluating dispersion equivalence
Jan, S., & Shieh, G. (2020) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2020.1821762
“Second, although Wellek (2010) presented the fundamental results of UMPT for dispersion equivalence analysis, it is of practical importance to enhance the usefulness of the UMPT approach by constructing the supporting computer programs.”
BackgroundMethodInfluentialPflanzner, T., Duan, Y., Bui, U. N., et al. (2020) · PDA Journal of Pharmaceutical Science and Technology · doi:10.5731/pdajpst.2020.012096
Performance Evaluation of Warped Virtual Surfaces in Virtual Reality
Didehkhorshid, S. A. A. (2020) · doi:10.22215/etd/2020-14194
Falsification, Pseudoscience, Induction
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.005
Beyond Probabilism and Performance
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.002 · Cited by 1
Comparing Distributions with the Probability of Agreement
Dadbin, M. (2021) · UWSpace (University of Waterloo)
Test for non-negligible adverse shiftsPreprint
Kamulete, V. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02990 · Cited by 1
“This is reminiscent of noninferiority tests [Wellek, 2010], widely used in healthcare to determine if a new treatment is in fact not inferior to an older one.”
BackgroundMethodMayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.004
Superiority, Non-inferiority, Equivalence Test, and Innovative Equivalence Test
Wanitjirattikal, P. (2021) · International Conference on Mathematics and Statistics (ICoMS) · doi:10.1145/3475827.3475841
“The effect size is a specified value (margin or minimal clinically important difference, δ ) [3].”
BackgroundTesting semiparametric model-equivalence hypotheses based on the characteristic functionPreprint
Chen, F., Meintanis, S. G., & Zhu, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.00280
“We close this paragraph by stressing that the range of possible values of ∆ ( M ) w,Z , when placed in the appropriate context, is important in order to acquire a feeling about meaningful values of the threshold parameter ∆, which is again heavily context-dependent; see for instance [4, § 1.7].”
BackgroundInfluentialPragmatic and Error Statistical Bayesians
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.017
What Ever Happened to Bayesian Foundations?
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.016
DENSITIES OF SOME MOLTEN SALT MIXTURES CONTAINING NITRATES
Smith, G. P., Petersen, G. F., & Ewing, W. M. (1959) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
Flores, P., Salicrú, M., Sánchez‐Pla, Á., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1281033/v1
Choudhary, P. K., & Nagaraja, H. N. (2017) · Wiley series in probability and statistics · doi:10.1002/9781118553282.refs
Senn, S. J. (2021) · doi:10.1002/9781119238614.ch22
Active Control Equivalence Studies
Senn, S. J. (2021) · doi:10.1002/9781119238614.ch15
Towards a New Paradigm for Statistical Evidence
Kim, J. H., & Bhatti, M. I. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-0883-2
What do we learn from an equivalence study without statistical power?Preprint
Peyton, K., & Solnick, R. E. (2018) · doi:10.31235/osf.io/rgz7c
Evaluating Equivalence Testing Methods for Measurement InvariancePreprint
Counsell, A., Cribbie, R. A., & Flora, D. B. (2020) · doi:10.31234/osf.io/rd5sc
Vidaković, B. (2011) · Springer texts in statistics · doi:10.1007/978-1-4614-0394-4_10
Bedbur, S., & Kamps, U. (2021) · SpringerBriefs in statistics · doi:10.1007/978-3-030-81900-2_5
Non-parametric Testing of Non-inferiority with Censored Data
Franco‐Pereira, A. M., Pardo, M. D. C., & Pérez, T. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-031-04137-2_28
Equivalence Tests for Multinomial Data Based on $$\phi $$-Divergences
Fernández, M. V. A., & Jiménez-Gamero, M. D. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-031-04137-2_12
Mielke, J., & Jones, B. C. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_272
Correspondence Measures for Assessing Replication SuccessPreprint
Steiner, P. M., Sheehan, P., & Wong, V. C. (2022) · doi:10.31234/osf.io/6vy75
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.013
Rejection Fallacies: Who’s Exaggerating What?
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.010
More Auditing: Objectivity and Model Checking
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.012
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.018
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.006
Error Probing Tools versus Logics of Evidence
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.003
Optimal equivalence testing in exponential families
Zhao, R., & Paige, R. L. (2022) · Statistical Papers · doi:10.1007/s00362-022-01346-4
BOB: Bayesian optimal design for biosimilar trials with co‐primary endpoints
Chi, X., Yu, Z., & Lin, R. (2022) · Statistics in Medicine · doi:10.1002/sim.9571
Auditing: Biasing Selection Effects and Randomization
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.011
Capability and Severity: Deeper Concepts
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.008
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.007
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.001
Deconstructing the N-P versus Fisher Debates
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.015
Mayo, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107286184.019
Luh, W., & Guo, J. (2022) · The International Journal of Biostatistics · doi:10.1515/ijb-2022-0039
Effects of Incremental Changes to Frame Mass on Manual Wheelchair Propulsion Cost
Misch, J., & Sprigle, S. H. (2023) · ASME Open Journal of Engineering · doi:10.1115/1.4062696
Metadata
- Authors
- Wellek, S.
- Corresponding author
- Wellek, Stefan
- ORCID
- Wellek, Stefan
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2010-06-24
- OpenAlex
- W2480935928
- Cited by
- 548
- References
- 0
- Citations by year
- 2026: 23 · 2025: 19 · 2024: 31 · 2023: 30 · 2022: 50 · 2021: 57 · 2020: 69 · 2019: 49 · 2018: 55 · 2017: 30 · 2016: 30 · 2015: 37 · 2014: 19 · 2013: 24 · 2012: 19
- FWCI
- 4.76
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- equivalence testing, non-inferiority testing, hypothesis testing, one-sample problem, clinical trials
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
