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Network meta‐analysis of rare events using penalized likelihood regression

Network meta‐analysis (NMA) of rare events has attracted little attention in the literature. Until recently, networks of interventions with rare events were analyzed using the inverse‐variance NMA approach. However, when events are rare the normal approximations made by this model can be poor and ef...

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Autores principales: Evrenoglou, Theodoros, White, Ian R., Afach, Sivem, Mavridis, Dimitris, Chaimani, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805041/
https://www.ncbi.nlm.nih.gov/pubmed/36054668
http://dx.doi.org/10.1002/sim.9562
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author Evrenoglou, Theodoros
White, Ian R.
Afach, Sivem
Mavridis, Dimitris
Chaimani, Anna
author_facet Evrenoglou, Theodoros
White, Ian R.
Afach, Sivem
Mavridis, Dimitris
Chaimani, Anna
author_sort Evrenoglou, Theodoros
collection PubMed
description Network meta‐analysis (NMA) of rare events has attracted little attention in the literature. Until recently, networks of interventions with rare events were analyzed using the inverse‐variance NMA approach. However, when events are rare the normal approximations made by this model can be poor and effect estimates are potentially biased. Other methods for the synthesis of such data are the recent extension of the Mantel‐Haenszel approach to NMA or the use of the noncentral hypergeometric distribution. In this article, we suggest a new common‐effect NMA approach that can be applied even in networks of interventions with extremely low or even zero number of events without requiring study exclusion or arbitrary imputations. Our method is based on the implementation of the penalized likelihood function proposed by Firth for bias reduction of the maximum likelihood estimate to the logistic expression of the NMA model. A limitation of our method is that heterogeneity cannot be taken into account as an additive parameter as in most meta‐analytical models. However, we account for heterogeneity by incorporating a multiplicative overdispersion term using a two‐stage approach. We show through simulation that our method performs consistently well across all tested scenarios and most often results in smaller bias than other available methods. We also illustrate the use of our method through two clinical examples. We conclude that our “penalized likelihood NMA” approach is promising for the analysis of binary outcomes with rare events especially for networks with very few studies per comparison and very low control group risks.
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spelling pubmed-98050412023-01-06 Network meta‐analysis of rare events using penalized likelihood regression Evrenoglou, Theodoros White, Ian R. Afach, Sivem Mavridis, Dimitris Chaimani, Anna Stat Med Research Articles Network meta‐analysis (NMA) of rare events has attracted little attention in the literature. Until recently, networks of interventions with rare events were analyzed using the inverse‐variance NMA approach. However, when events are rare the normal approximations made by this model can be poor and effect estimates are potentially biased. Other methods for the synthesis of such data are the recent extension of the Mantel‐Haenszel approach to NMA or the use of the noncentral hypergeometric distribution. In this article, we suggest a new common‐effect NMA approach that can be applied even in networks of interventions with extremely low or even zero number of events without requiring study exclusion or arbitrary imputations. Our method is based on the implementation of the penalized likelihood function proposed by Firth for bias reduction of the maximum likelihood estimate to the logistic expression of the NMA model. A limitation of our method is that heterogeneity cannot be taken into account as an additive parameter as in most meta‐analytical models. However, we account for heterogeneity by incorporating a multiplicative overdispersion term using a two‐stage approach. We show through simulation that our method performs consistently well across all tested scenarios and most often results in smaller bias than other available methods. We also illustrate the use of our method through two clinical examples. We conclude that our “penalized likelihood NMA” approach is promising for the analysis of binary outcomes with rare events especially for networks with very few studies per comparison and very low control group risks. John Wiley & Sons, Inc. 2022-08-26 2022-11-20 /pmc/articles/PMC9805041/ /pubmed/36054668 http://dx.doi.org/10.1002/sim.9562 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Evrenoglou, Theodoros
White, Ian R.
Afach, Sivem
Mavridis, Dimitris
Chaimani, Anna
Network meta‐analysis of rare events using penalized likelihood regression
title Network meta‐analysis of rare events using penalized likelihood regression
title_full Network meta‐analysis of rare events using penalized likelihood regression
title_fullStr Network meta‐analysis of rare events using penalized likelihood regression
title_full_unstemmed Network meta‐analysis of rare events using penalized likelihood regression
title_short Network meta‐analysis of rare events using penalized likelihood regression
title_sort network meta‐analysis of rare events using penalized likelihood regression
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805041/
https://www.ncbi.nlm.nih.gov/pubmed/36054668
http://dx.doi.org/10.1002/sim.9562
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