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Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study
BACKGROUND: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we pre...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948351/ https://www.ncbi.nlm.nih.gov/pubmed/36823545 http://dx.doi.org/10.1186/s12874-023-01864-1 |
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author | Deng, Lejun Hsu, Chih-Yuan Shyr, Yu |
author_facet | Deng, Lejun Hsu, Chih-Yuan Shyr, Yu |
author_sort | Deng, Lejun |
collection | PubMed |
description | BACKGROUND: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. METHODS: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. RESULTS: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. CONCLUSIONS: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01864-1. |
format | Online Article Text |
id | pubmed-9948351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99483512023-02-24 Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study Deng, Lejun Hsu, Chih-Yuan Shyr, Yu BMC Med Res Methodol Research BACKGROUND: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. METHODS: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. RESULTS: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. CONCLUSIONS: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01864-1. BioMed Central 2023-02-23 /pmc/articles/PMC9948351/ /pubmed/36823545 http://dx.doi.org/10.1186/s12874-023-01864-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Deng, Lejun Hsu, Chih-Yuan Shyr, Yu Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title | Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title_full | Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title_fullStr | Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title_full_unstemmed | Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title_short | Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
title_sort | power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948351/ https://www.ncbi.nlm.nih.gov/pubmed/36823545 http://dx.doi.org/10.1186/s12874-023-01864-1 |
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