S͙U͙P͙E͙R͙I͙O͙R͙I͙T͙Y͙ A͙N͙D͙ E͙Q͙U͙I͙V͙A͙L͙E͙N͙C͙E͙ T͙R͙I͙A͙L͙S͙ ⬆️↔️

⬆️S͙U͙P͙E͙R͙I͙O͙R͙I͙T͙Y͙ T͙R͙I͙A͙L͙S͙

✔️Seek to establish that one treatment is better than another 

✔️The sample size is set so that there is high statistical power to detect a clinically meaningful difference between the two treatments 

↔️E͙Q͙U͙I͙V͙A͙L͙E͙N͙C͙E͙ T͙R͙I͙A͙L͙S͙

✔️Seek to test if a new treatment is similar  in effectiveness to an existing one

✔️Appropriate if the new treatment has certain benefits such as fewer side effects, being easier to use, or being cheaper 

✔️Designed to be able to demonstrate that, within given acceptable limits, the two treatments are equally effective 

✔️Equivalence is a pre-set maximum difference between treatments such that, if the observed difference is less than this, the two treatments are regarded as equivalent . The tighter the limits of equivalence are set, the larger the sample size that will be required 

✔️A serious condition requires tighter limits for equivalence than a less serious condition. 

✔️The calculated sample size tends to be bigger for equivalence trials than superiority trials 

🔴T͙H͙I͙N͙G͙S͙ T͙O͙ R͙E͙M͙E͙M͙B͙E͙R͙

👉🏿In general the design and implementation of equivalence trials is less straight forward than superiority trials 

👉🏿If patients are lost to follow-up or fail to comply with the trial protocol, then any differences between the treatments is likely to be reduced and so equivalence may be incorrectly inferred.

👉🏿So equivalence trials need very strict management and good patient follow-up to minimize these problems 

👉🏿It is often helpful to include a secondary analysis where subjects are analysed according to the treatment they actually received, ‘per protocol’ analysis 

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Reference: Oxford Handbook of Medical Statistics, Janet L. Peacock , Philip J. Peacock