testing statistical hypotheses of equivalence and noninferiority pdf

Testing Statistical Hypotheses Of Equivalence And Noninferiority Pdf

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Equivalence test

Edward J. When a test for superiority is statistically significant, we happily conclude that one method is better than the other, especially if it is in the expected direction! But what can it mean if the test is not significant? Actually, no, because although it could be that no clinically meaningful population difference exists, it is also possible that one does exist—but either the study was underpowered to detect it sample size was too small or we just got unlucky false negative result. So from a nonsignificant test for superiority, we can really conclude only that no population difference was detected, and not equivalence, even if the observed means are very similar!

Fortunately, accepted methods of assessing and claiming equivalence between two randomized interventions do exist. Suppose an intervention is known to have favorable intraoperative properties on certain key parameters but is suspected of adversely affecting other parameters.

For example, Bala et al. Because no effect i. The alternative hypothesis H 1 , which one wishes to conclude, is that the true difference lies between the specified limits i. The depicted conclusions can only be drawn if the study was designed to test the corresponding hypothesis superiority, noninferiority, equivalence.

Sometimes it is more natural to express the equivalence region in terms of ratios of means than as an absolute value. When a ratio formulation is used, hypotheses are best specified on the log scale to create symmetric equivalency limits.

Demonstrating superiority on rate of rewarming or intraoperative temperature of the new device over the existing one would be a luxury, as it would suffice to show that the new device was at least not worse than the existing device i. Noninferiority designs are useful when the goal is to show that a new treatment is at least as effective as the standard i. When the noninferiority test is significant e.

For superiority designs, a two-sided superiority test significant in either direction corresponds to a CI that does not contain zero, as in the first example A in figure 1 , where the test treatment is found to be superior to standard.

The second superiority CI B contains zero, so the test must be nonsignificant, and we conclude that no difference was found.

Equivalence cannot be claimed here because it was not tested and because no definition of equivalence is specified in the design of a superiority trial. This prompts a question: in a trial designed for superiority, can researchers test for noninferiority after a nonsignificant test of superiority? No, a nonsignificant superiority test ends the testing, because further testing would increase the chance of type I error for which the trial was designed.

However, it would be acceptable to assess superiority in a noninferiority trial after noninferiority had been established, because a significant noninferiority result implies potential superiority. In other words, additional testing to refine a statistically significant result is appropriate, but changing hypotheses to find statistical significance is not.

If there are prior data and good intuition suggesting that the underlying difference is nonzero, the sample size may be calculated assuming a nonzero effect. For a noninferiority trial in which the underlying difference favored the preferred treatment, the sample size would be decreased.

In reporting results, studies designed to assess equivalency and noninferiority 10,11 should be clearly labeled as such. Incorporating these readily available and widely accepted methods for assessing equivalency and noninferiority will strengthen clinical trial design and reporting. Sign In or Create an Account.

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Testing Statistical Hypotheses of Equivalence and Noninferiority

By: Dr. Neil Polhemus. Published under: new feature , statistical analysis , data analysis , Data analytics , Quality , Statgraphics , analytics software , equivalence tests , equivalence trials , equivalence and noninferiority trials. Important additions to Statgraphics 18 are 4 procedures for equivalence and noninferiority testing: comparison of 2 independent means, comparison of 2 paired means, comparison of 2 means using a 2x2 crossover study, and comparison of a mean to a target value. In each case, the tests are designed to demonstrate that a test formulation or treatment gives equivalent or better results than a reference treatment. This is in marked contrast to most hypothesis tests that are designed to demonstrate differences rather than similarities. Of course, equivalent doesn't mean exactly the same.

The idea of CET is carefully considered as it has the potential to address recent concerns about reproducibility and the limited publication of null results. In this paper we detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: There is no original data discussed in this manuscript. Simulated data was generated to produce a number of the figures presented. We have moved all of our code that can reproduce all of our results and figures to a publicly available repository on the Open Science Framework Identifiers: DOI


While continuing to focus on methods of testing for two-sided equivalence, Testing Statistical Hypotheses of Equivalence and Noninferiority, Second Edition​.


Equivalence test

Edward J. When a test for superiority is statistically significant, we happily conclude that one method is better than the other, especially if it is in the expected direction! But what can it mean if the test is not significant? Actually, no, because although it could be that no clinically meaningful population difference exists, it is also possible that one does exist—but either the study was underpowered to detect it sample size was too small or we just got unlucky false negative result.

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 of fixed known variance to problems involving several dependent or independent samples and multivariate data. Along with expanding the material on noninferiority problems, this edition includes new chapters on equivalence tests for multivariate data and tests for relevant differences between treatments. A majority of the computer programs offered online are now available not only in SAS or Fortran but also as R scripts or as shared objects that can be called within the R system. This book provides readers with a rich repertoire of efficient solutions to specific equivalence and noninferiority testing problems frequently encountered in the analysis of real data sets.

Normally, randomized controlled trials RCTs aim to prove that a new drug has better or superior efficacy than standard treatment or placebo. In fact, RCTs can also be used to evaluate the efficacy of a new drug having similar or equivalence efficacy, or not worse or non-inferior efficacy depending on the objectives of the research. Meanwhile, the non-inferiority trials are more frequently found in research. The aim of this article is to provide a basic understanding for readers about the distinctions among the types of research, statistical hypothesis testing, the interpretation of hypothesis testing as well as the differences between statistical significance and clinical significance. In the case that some parts are used by others The author must Confirm that obtaining permission to use some of the original authors.

Старик вздохнул.

Testing Statistical Hypotheses of Equivalence and Noninferiority

Целясь в торс, он сводил к минимуму возможность промаха в вертикальной и горизонтальной плоскостях. Эта тактика себя оправдала. Хотя в последнее мгновение Беккер увернулся, Халохот сумел все же его зацепить. Он понимал, что пуля лишь слегка оцарапала жертву, не причинив существенного ущерба, тем не менее она сделала свое. Контакт был установлен.

Ее мысли прервал шипящий звук открываемой пневматической двери. В Третий узел заглянул Стратмор. - Какие-нибудь новости, Сьюзан? - спросил Стратмор и тут же замолчал, увидав Грега Хейла.  - Добрый вечер, мистер Хейл.

А в своем пиджаке он обречен. Беккер понимал, что в данный момент ничего не может предпринять. Ему оставалось только стоять на коленях на холодном каменном полу огромного собора. Старик утратил к нему всякий интерес, прихожане встали и запели гимн. Ноги у него свело судорогой. Хорошо бы их вытянуть. Терпи, - сказал он .


Request PDF | On Apr 1, , David J. Hand published Testing Statistical Hypotheses of Equivalence and Noninferiority, Second Edition by.


Двигаясь в дыму, она вдруг вспомнила слова Хейла: У этого лифта автономное электропитание, идущее из главного здания. Я видел схему. Она знала, что это .

Все будет прекрасно. Приближаясь к пиджаку защитного цвета, он не обращал внимания на сердитый шепот людей, которых обгонял. Прихожане могли понять нетерпение этого человека, стремившегося получить благословение, но ведь существуют строгие правила протокола: подходить к причастию нужно, выстроившись в две линии.

Двигаясь в дыму, она вдруг вспомнила слова Хейла: У этого лифта автономное электропитание, идущее из главного здания. Я видел схему. Она знала, что это .

Соши открутила несколько страниц. Механизм атомной бомбы A) альтиметр B) детонатор сжатого воздуха C) детонирующие головки D) взрывчатые заряды E) нейтронный дефлектор F) уран и плутоний G) свинцовая защита Н) взрыватели II. Ядерное делениеядерный синтез A) деление (атомная бомба) и синтез (водородная бомба) B) U-235, U-238 и плутоний III.

Самая грязная ванна, какую мне доводилось видеть. И самый мерзкий пляж, покрытый острыми камнями. Этого и ждут от меня читатели.

У нас нет гарантий, что Дэвид найдет вторую копию. Если по какой-то случайности кольцо попадет не в те руки, я бы предпочел, чтобы мы уже внесли нужные изменения в алгоритм. Тогда, кто бы ни стал обладателем ключа, он скачает себе нашу версию алгоритма.

Something to say?

Их надо использовать с толком. Фонтейн долго молчал. Потом, тяжело вздохнув, скомандовал: - Хорошо.

2 comments

Selene S.

Equivalence tests are a variation of hypothesis tests used to draw statistical inferences from observed data.

REPLY

Louis V.

While continuing to focus on methods of testing for two-sided equivalence, Testing Statistical Hypotheses of Equivalence and Noninferiority.

REPLY

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