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Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study
A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439130/ https://www.ncbi.nlm.nih.gov/pubmed/37596461 http://dx.doi.org/10.1038/s41598-023-40538-2 |
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author | Safari, Malihe Esmaeili, Habib Mahjub, Hossein Roshanaei, Ghodratollah |
author_facet | Safari, Malihe Esmaeili, Habib Mahjub, Hossein Roshanaei, Ghodratollah |
author_sort | Safari, Malihe |
collection | PubMed |
description | A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch to the experimental treatment. Treatment switching in a randomized controlled trial refers to a situation in which patients switch from their randomized treatment to another treatment. Often, the switchis from the control group to the experimental treatment. In this case, the treatment effect estimate is adjusted using either convenient naive methods such as intention-to-treat, per-protocol or advanced methods such as rank preserving structural failure time (RPSFT) models. In previous simulation studies performed so far, there was only one possible outcome for patients. However, in oncology in particular, multiple outcomes are potentially possible. These outcomes are called competing risks. This aspect has not been considered in previous studies when determining the effect of a treatment in the presence of noncompliance. This study aimed to extend the RPSFT method using a two-dimensional G-estimation in the presence of competing risks. The RPSFT method was extended for two events, the event of interest and the competing event. For this purpose, the RPSFT method was applied based on the cause-specific hazard approach, the result of which is compared to the naive methods used in simulation studies. The results show that the proposed method has a good performance compared to other methods. |
format | Online Article Text |
id | pubmed-10439130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104391302023-08-20 Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study Safari, Malihe Esmaeili, Habib Mahjub, Hossein Roshanaei, Ghodratollah Sci Rep Article A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch to the experimental treatment. Treatment switching in a randomized controlled trial refers to a situation in which patients switch from their randomized treatment to another treatment. Often, the switchis from the control group to the experimental treatment. In this case, the treatment effect estimate is adjusted using either convenient naive methods such as intention-to-treat, per-protocol or advanced methods such as rank preserving structural failure time (RPSFT) models. In previous simulation studies performed so far, there was only one possible outcome for patients. However, in oncology in particular, multiple outcomes are potentially possible. These outcomes are called competing risks. This aspect has not been considered in previous studies when determining the effect of a treatment in the presence of noncompliance. This study aimed to extend the RPSFT method using a two-dimensional G-estimation in the presence of competing risks. The RPSFT method was extended for two events, the event of interest and the competing event. For this purpose, the RPSFT method was applied based on the cause-specific hazard approach, the result of which is compared to the naive methods used in simulation studies. The results show that the proposed method has a good performance compared to other methods. Nature Publishing Group UK 2023-08-18 /pmc/articles/PMC10439130/ /pubmed/37596461 http://dx.doi.org/10.1038/s41598-023-40538-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Safari, Malihe Esmaeili, Habib Mahjub, Hossein Roshanaei, Ghodratollah Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title | Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title_full | Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title_fullStr | Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title_full_unstemmed | Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title_short | Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
title_sort | estimation of treatment effect in presence of noncompliance and competing risks: a simulation study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439130/ https://www.ncbi.nlm.nih.gov/pubmed/37596461 http://dx.doi.org/10.1038/s41598-023-40538-2 |
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