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Why ITT analysis is not always the answer for estimating treatment effects in clinical trials
For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to ana...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234249/ https://www.ncbi.nlm.nih.gov/pubmed/34186242 http://dx.doi.org/10.1016/j.cct.2021.106494 |
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author | Keene, Oliver N. Wright, David Phillips, Alan Wright, Melanie |
author_facet | Keene, Oliver N. Wright, David Phillips, Alan Wright, Melanie |
author_sort | Keene, Oliver N. |
collection | PubMed |
description | For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial “estimand”, a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic. |
format | Online Article Text |
id | pubmed-8234249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82342492021-06-28 Why ITT analysis is not always the answer for estimating treatment effects in clinical trials Keene, Oliver N. Wright, David Phillips, Alan Wright, Melanie Contemp Clin Trials Article For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial “estimand”, a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic. Published by Elsevier Inc. 2021-09 2021-06-26 /pmc/articles/PMC8234249/ /pubmed/34186242 http://dx.doi.org/10.1016/j.cct.2021.106494 Text en © 2021 Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Keene, Oliver N. Wright, David Phillips, Alan Wright, Melanie Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title | Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title_full | Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title_fullStr | Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title_full_unstemmed | Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title_short | Why ITT analysis is not always the answer for estimating treatment effects in clinical trials |
title_sort | why itt analysis is not always the answer for estimating treatment effects in clinical trials |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234249/ https://www.ncbi.nlm.nih.gov/pubmed/34186242 http://dx.doi.org/10.1016/j.cct.2021.106494 |
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