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When does the use of individual patient data in network meta-analysis make a difference? A simulation study
BACKGROUND: The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD on...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805229/ https://www.ncbi.nlm.nih.gov/pubmed/33435879 http://dx.doi.org/10.1186/s12874-020-01198-2 |
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author | Kanters, Steve Karim, Mohammad Ehsanul Thorlund, Kristian Anis, Aslam Bansback, Nick |
author_facet | Kanters, Steve Karim, Mohammad Ehsanul Thorlund, Kristian Anis, Aslam Bansback, Nick |
author_sort | Kanters, Steve |
collection | PubMed |
description | BACKGROUND: The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. METHODS: Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. RESULTS: Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. CONCLUSIONS: Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-020-01198-2. |
format | Online Article Text |
id | pubmed-7805229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78052292021-01-14 When does the use of individual patient data in network meta-analysis make a difference? A simulation study Kanters, Steve Karim, Mohammad Ehsanul Thorlund, Kristian Anis, Aslam Bansback, Nick BMC Med Res Methodol Research Article BACKGROUND: The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. METHODS: Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. RESULTS: Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. CONCLUSIONS: Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-020-01198-2. BioMed Central 2021-01-13 /pmc/articles/PMC7805229/ /pubmed/33435879 http://dx.doi.org/10.1186/s12874-020-01198-2 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Kanters, Steve Karim, Mohammad Ehsanul Thorlund, Kristian Anis, Aslam Bansback, Nick When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title | When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title_full | When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title_fullStr | When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title_full_unstemmed | When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title_short | When does the use of individual patient data in network meta-analysis make a difference? A simulation study |
title_sort | when does the use of individual patient data in network meta-analysis make a difference? a simulation study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805229/ https://www.ncbi.nlm.nih.gov/pubmed/33435879 http://dx.doi.org/10.1186/s12874-020-01198-2 |
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