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

<h4>Introduction</h4>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-eve...

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Autores principales:	Matthieu Faron, Pierre Blanchard, Laureen Ribassin-Majed, Jean-Pierre Pignon, Stefan Michiels, Gwénaël Le Teuff
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b2536db423394dc4b129509fadcc7c10
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spelling	oai:doaj.org-article:b2536db423394dc4b129509fadcc7c102021-12-02T20:04:35ZA frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier.1932-620310.1371/journal.pone.0259121https://doaj.org/article/b2536db423394dc4b129509fadcc7c102021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259121https://doaj.org/toc/1932-6203<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.Matthieu FaronPierre BlanchardLaureen Ribassin-MajedJean-Pierre PignonStefan MichielsGwénaël Le TeuffPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259121 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthieu Faron Pierre Blanchard Laureen Ribassin-Majed Jean-Pierre Pignon Stefan Michiels Gwénaël Le Teuff A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier.
description	<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.
format	article
author	Matthieu Faron Pierre Blanchard Laureen Ribassin-Majed Jean-Pierre Pignon Stefan Michiels Gwénaël Le Teuff
author_facet	Matthieu Faron Pierre Blanchard Laureen Ribassin-Majed Jean-Pierre Pignon Stefan Michiels Gwénaël Le Teuff
author_sort	Matthieu Faron
title	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_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_sort	frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b2536db423394dc4b129509fadcc7c10
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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.

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