Sample size calculation in randomised phase II selection trials using a margin of practical equivalence.
Randomised trial
Rare cancers
Selection design
Journal
Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253
Informations de publication
Date de publication:
30 Mar 2020
30 Mar 2020
Historique:
received:
23
08
2019
accepted:
12
03
2020
entrez:
2
4
2020
pubmed:
2
4
2020
medline:
20
1
2021
Statut:
epublish
Résumé
In rare cancers or subtypes of common cancers, a comparison of multiple promising treatments may be required. The selected treatment can then be assessed against the standard of care (if it exists) or used as a backbone for combinations with new, possibly targeted, agents. There could be different experimental therapies or different doses of the same therapy, and either can be done in combination with standard treatments. A 'pick-the-winner' design is often used, which focuses on efficacy to select the most promising treatment. However, a treatment with a slightly lower efficacy compared to another treatment may actually be preferred if it has a better toxicity or quality of life profile, is easier to administer, or cheaper. By pre-defining a margin of practical equivalence in order to calculate the sample size, a more flexible assessment can be made of whether the treatments have very different effects or are sufficiently close so that other factors can be used to choose between them. Using exact binomial probabilities, we calculated the sample size for two- and three-arm randomised selection trials including a margin of practical equivalence with a variety of input parameters. We explain conceptually the margin of practical equivalence in this paper, and provide a free user-friendly web application to calculate the required sample size for a variety of input parameters. The web application should help promote the randomised selection design with a margin of practical equivalence, which provides greater flexibility than the 'pick-the-winner' approach in assessing the results of selection trials.
Sections du résumé
BACKGROUND
BACKGROUND
In rare cancers or subtypes of common cancers, a comparison of multiple promising treatments may be required. The selected treatment can then be assessed against the standard of care (if it exists) or used as a backbone for combinations with new, possibly targeted, agents. There could be different experimental therapies or different doses of the same therapy, and either can be done in combination with standard treatments. A 'pick-the-winner' design is often used, which focuses on efficacy to select the most promising treatment. However, a treatment with a slightly lower efficacy compared to another treatment may actually be preferred if it has a better toxicity or quality of life profile, is easier to administer, or cheaper.
METHODS
METHODS
By pre-defining a margin of practical equivalence in order to calculate the sample size, a more flexible assessment can be made of whether the treatments have very different effects or are sufficiently close so that other factors can be used to choose between them. Using exact binomial probabilities, we calculated the sample size for two- and three-arm randomised selection trials including a margin of practical equivalence with a variety of input parameters.
RESULTS
RESULTS
We explain conceptually the margin of practical equivalence in this paper, and provide a free user-friendly web application to calculate the required sample size for a variety of input parameters.
CONCLUSION
CONCLUSIONS
The web application should help promote the randomised selection design with a margin of practical equivalence, which provides greater flexibility than the 'pick-the-winner' approach in assessing the results of selection trials.
Identifiants
pubmed: 32228674
doi: 10.1186/s13063-020-04248-8
pii: 10.1186/s13063-020-04248-8
pmc: PMC7106856
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
301Subventions
Organisme : Cancer Research UK
ID : C444/A15953
Pays : United Kingdom
Références
CA Cancer J Clin. 2017 Jul 8;67(4):261-272
pubmed: 28542893
Stat Med. 2001 Apr 15;20(7):1051-60
pubmed: 11276035
Eur J Cancer. 2011 Nov;47(17):2493-511
pubmed: 22033323
Lancet Oncol. 2016 Feb;17(2):e52-e61
pubmed: 26868354
Trials. 2016 Sep 20;17(1):456
pubmed: 27645620
Cancer Treat Rep. 1985 Dec;69(12):1375-81
pubmed: 4075313
BMC Cancer. 2015 Feb 12;15:48
pubmed: 25880814
Eur J Cancer. 2015 Feb;51(3):271-81
pubmed: 25542058
Lancet Oncol. 2013 Feb;14(2):109-10
pubmed: 23369681