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A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

INTRODUCTION: Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to bina...

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Autores principales: Faron, Matthieu, Blanchard, Pierre, Ribassin-Majed, Laureen, Pignon, Jean-Pierre, Michiels, Stefan, Le Teuff, Gwénaël
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559936/
https://www.ncbi.nlm.nih.gov/pubmed/34723994
http://dx.doi.org/10.1371/journal.pone.0259121
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author Faron, Matthieu
Blanchard, Pierre
Ribassin-Majed, Laureen
Pignon, Jean-Pierre
Michiels, Stefan
Le Teuff, Gwénaël
author_facet Faron, Matthieu
Blanchard, Pierre
Ribassin-Majed, Laureen
Pignon, Jean-Pierre
Michiels, Stefan
Le Teuff, Gwénaël
author_sort Faron, Matthieu
collection PubMed
description INTRODUCTION: Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data. METHODS: One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference. RESULTS: In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression. CONCLUSION: The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.
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spelling pubmed-85599362021-11-02 A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier Faron, Matthieu Blanchard, Pierre Ribassin-Majed, Laureen Pignon, Jean-Pierre Michiels, Stefan Le Teuff, Gwénaël PLoS One Research Article INTRODUCTION: Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data. METHODS: One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference. RESULTS: In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression. CONCLUSION: The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible. Public Library of Science 2021-11-01 /pmc/articles/PMC8559936/ /pubmed/34723994 http://dx.doi.org/10.1371/journal.pone.0259121 Text en © 2021 Faron et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Faron, Matthieu
Blanchard, Pierre
Ribassin-Majed, Laureen
Pignon, Jean-Pierre
Michiels, Stefan
Le Teuff, Gwénaël
A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title_full A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title_fullStr A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title_full_unstemmed A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title_short A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
title_sort frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559936/
https://www.ncbi.nlm.nih.gov/pubmed/34723994
http://dx.doi.org/10.1371/journal.pone.0259121
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