Safety equivalence studies may be required to demonstrate that a new procedure or process is at least as safe as a previous one. They usually involve low or very low outcome rates that are often not precisely determined, making patient-based sample sizing uncertain. Using a reverse sampling approach, a method is derived from standard equations to estimate the number of events that need to be observed to demonstrate equivalence using the confidence interval approach. For instance, for a one-sided (nonsuperiority) hypothesis, 5% alpha risk, and 80% power, almost 100 events need to be observed in each study arm to demonstrate equivalence within 30%, or 250 events for 20% equivalence. The number of patients to be included can be derived directly from expected event rates.
%0 Journal Article
%1 Moore2003
%A Moore, Nicholas
%A Tubert-Bitter, Pascale
%A Fourrier, Anne
%A Bégaud, Bernard
%D 2003
%J Journal of clinical epidemiology
%K ConfidenceIntervals EpidemiologicResearchDesign Humans Safety SampleSize TherapeuticEquivalency
%N 5
%P 433-5
%T A simple method to estimate sample sizes for safety equivalence studies using inverse sampling.
%U http://www.ncbi.nlm.nih.gov/pubmed/12812816
%V 56
%X Safety equivalence studies may be required to demonstrate that a new procedure or process is at least as safe as a previous one. They usually involve low or very low outcome rates that are often not precisely determined, making patient-based sample sizing uncertain. Using a reverse sampling approach, a method is derived from standard equations to estimate the number of events that need to be observed to demonstrate equivalence using the confidence interval approach. For instance, for a one-sided (nonsuperiority) hypothesis, 5% alpha risk, and 80% power, almost 100 events need to be observed in each study arm to demonstrate equivalence within 30%, or 250 events for 20% equivalence. The number of patients to be included can be derived directly from expected event rates.
@article{Moore2003,
abstract = {Safety equivalence studies may be required to demonstrate that a new procedure or process is at least as safe as a previous one. They usually involve low or very low outcome rates that are often not precisely determined, making patient-based sample sizing uncertain. Using a reverse sampling approach, a method is derived from standard equations to estimate the number of events that need to be observed to demonstrate equivalence using the confidence interval approach. For instance, for a one-sided (nonsuperiority) hypothesis, 5% alpha risk, and 80% power, almost 100 events need to be observed in each study arm to demonstrate equivalence within 30%, or 250 events for 20% equivalence. The number of patients to be included can be derived directly from expected event rates.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Moore, Nicholas and Tubert-Bitter, Pascale and Fourrier, Anne and Bégaud, Bernard},
biburl = {https://www.bibsonomy.org/bibtex/21952cff6b269be90e0d0e928cbb62e29/jepcastel},
interhash = {9b65edf7ee311b54edb6414ba06e75fb},
intrahash = {1952cff6b269be90e0d0e928cbb62e29},
issn = {0895-4356},
journal = {Journal of clinical epidemiology},
keywords = {ConfidenceIntervals EpidemiologicResearchDesign Humans Safety SampleSize TherapeuticEquivalency},
month = {5},
note = {3542<m:linebreak></m:linebreak>Sample size},
number = 5,
pages = {433-5},
pmid = {12812816},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {A simple method to estimate sample sizes for safety equivalence studies using inverse sampling.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12812816},
volume = 56,
year = 2003
}