Cargando…

Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials

The Multiple Comparison Procedure – Modelling (MCP-Mod) method was qaulified by regulatory agencies (e.g., EMA in 2014 and FDA in 2016) as an efficient statistical method for Phase 2 dose-finding studies when there is uncertainty about dose-response relationship. As this is a relatively new approach...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jingjing, Liu, Tina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451792/
https://www.ncbi.nlm.nih.gov/pubmed/32875139
http://dx.doi.org/10.1016/j.conctc.2020.100641
_version_ 1783575051129323520
author Chen, Jingjing
Liu, Tina
author_facet Chen, Jingjing
Liu, Tina
author_sort Chen, Jingjing
collection PubMed
description The Multiple Comparison Procedure – Modelling (MCP-Mod) method was qaulified by regulatory agencies (e.g., EMA in 2014 and FDA in 2016) as an efficient statistical method for Phase 2 dose-finding studies when there is uncertainty about dose-response relationship. As this is a relatively new approach, there is limited literature providing practical guidance on its application. In this paper, we evaluated the performance of the MCP-Mod method for clinical trials with a binary primary endpoint, focusing on (1) the impact of sample size, data variability and treatment effect size on the performance of the MCP-Mod, (2) the impact of candidate model mis-specification, and (3) optimal sample allocation under a fixed sample size. The evaluation was performed via simulations under different scenarios.
format Online
Article
Text
id pubmed-7451792
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-74517922020-08-31 Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials Chen, Jingjing Liu, Tina Contemp Clin Trials Commun Article The Multiple Comparison Procedure – Modelling (MCP-Mod) method was qaulified by regulatory agencies (e.g., EMA in 2014 and FDA in 2016) as an efficient statistical method for Phase 2 dose-finding studies when there is uncertainty about dose-response relationship. As this is a relatively new approach, there is limited literature providing practical guidance on its application. In this paper, we evaluated the performance of the MCP-Mod method for clinical trials with a binary primary endpoint, focusing on (1) the impact of sample size, data variability and treatment effect size on the performance of the MCP-Mod, (2) the impact of candidate model mis-specification, and (3) optimal sample allocation under a fixed sample size. The evaluation was performed via simulations under different scenarios. Elsevier 2020-08-14 /pmc/articles/PMC7451792/ /pubmed/32875139 http://dx.doi.org/10.1016/j.conctc.2020.100641 Text en © 2020 The Authors. Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Chen, Jingjing
Liu, Tina
Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title_full Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title_fullStr Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title_full_unstemmed Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title_short Statistical considerations on implementing the MCP-Mod method for binary endpoints in clinical trials
title_sort statistical considerations on implementing the mcp-mod method for binary endpoints in clinical trials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451792/
https://www.ncbi.nlm.nih.gov/pubmed/32875139
http://dx.doi.org/10.1016/j.conctc.2020.100641
work_keys_str_mv AT chenjingjing statisticalconsiderationsonimplementingthemcpmodmethodforbinaryendpointsinclinicaltrials
AT liutina statisticalconsiderationsonimplementingthemcpmodmethodforbinaryendpointsinclinicaltrials