IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0259121.html
   My bibliography  Save this article

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

Author

Listed:
  • Matthieu Faron
  • Pierre Blanchard
  • Laureen Ribassin-Majed
  • Jean-Pierre Pignon
  • Stefan Michiels
  • Gwénaël Le Teuff

Abstract

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.

Suggested Citation

  • Matthieu Faron & Pierre Blanchard & Laureen Ribassin-Majed & Jean-Pierre Pignon & Stefan Michiels & Gwénaël Le Teuff, 2021. "A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
  • Handle: RePEc:plo:pone00:0259121
    DOI: 10.1371/journal.pone.0259121
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259121
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0259121&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0259121?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yu-Kang Tu, 2014. "Use of Generalized Linear Mixed Models for Network Meta-analysis," Medical Decision Making, , vol. 34(7), pages 911-918, October.
    2. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bart Verkuil & Serpil Atasayi & Marc L Molendijk, 2015. "Workplace Bullying and Mental Health: A Meta-Analysis on Cross-Sectional and Longitudinal Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    2. Francesca Pilotto & Ingolf Kühn & Rita Adrian & Renate Alber & Audrey Alignier & Christopher Andrews & Jaana Bäck & Luc Barbaro & Deborah Beaumont & Natalie Beenaerts & Sue Benham & David S. Boukal & , 2020. "Meta-analysis of multidecadal biodiversity trends in Europe," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. repec:cup:judgdm:v:15:y:2020:i:6:p:972-988 is not listed on IDEAS
    4. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    5. Mario Herberz & Tobias Brosch & Ulf J. J. Hahnel, 2020. "Kilo what? Default units increase value sensitivity in joint evaluations of energy efficiency," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(6), pages 972-988, November.
    6. Maier, Maximilian & VanderWeele, Tyler J. & Mathur, Maya B, 2021. "Using Selection Models to Assess Sensitivity to Publication Bias: A Tutorial and Call for More Routine Use," MetaArXiv tp45u_v1, Center for Open Science.
    7. Piers Steel & Sjoerd Beugelsdijk & Herman Aguinis, 2021. "The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(1), pages 23-44, February.
    8. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.
    9. Georgiou, George K. & Guo, Kan & Naveenkumar, Nithya & Vieira, Ana Paula Alves & Das, J.P., 2020. "PASS theory of intelligence and academic achievement: A meta-analytic review," Intelligence, Elsevier, vol. 79(C).
    10. Stephan Kambach & Ingolf Kühn & Bastien Castagneyrol & Helge Bruelheide, 2016. "The Impact of Tree Diversity on Different Aspects of Insect Herbivory along a Global Temperature Gradient - A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    11. Nan Wang & Yuxiang Luan & Rui Ma, 2024. "Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    12. repec:cup:judgdm:v:14:y:2019:i:3:p:234-279 is not listed on IDEAS
    13. de la Cruz, Vera Ysabel V. & Tantriani, & Cheng, Weiguo & Tawaraya, Keitaro, 2023. "Yield gap between organic and conventional farming systems across climate types and sub-types: A meta-analysis," Agricultural Systems, Elsevier, vol. 211(C).
    14. Kelly R Moran & Sara Y Del Valle, 2016. "A Meta-Analysis of the Association between Gender and Protective Behaviors in Response to Respiratory Epidemics and Pandemics," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-25, October.
    15. Cyrielle Maroteau & Antonio Espuela-Ortiz & Esther Herrera-Luis & Sundararajan Srinivasan & Fiona Carr & Roger Tavendale & Karen Wilson & Natalia Hernandez-Pacheco & James D Chalmers & Steve Turner & , 2021. "LTA4H rs2660845 association with montelukast response in early and late-onset asthma," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-17, September.
    16. Barne Willie & Emma L. Sweeney & Steven G. Badman & Mark Chatfield & Andrew J. Vallely & Angela Kelly-Hanku & David M. Whiley, 2022. "The Prevalence of Antimicrobial Resistant Neisseria gonorrhoeae in Papua New Guinea: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(3), pages 1-11, January.
    17. Larney, Andrea & Rotella, Amanda & Barclay, Pat, 2019. "Stake size effects in ultimatum game and dictator game offers: A meta-analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 61-72.
    18. Blum, Diego & Holling, Heinz, 2017. "Spearman's law of diminishing returns. A meta-analysis," Intelligence, Elsevier, vol. 65(C), pages 60-66.
    19. Stephanie Medlock & Juliette L Parlevliet & Danielle Sent & Saeid Eslami & Marjan Askari & Derk L Arts & Joost B Hoekstra & Sophia E de Rooij & Ameen Abu-Hanna, 2017. "An email-based intervention to improve the number and timeliness of letters sent from the hospital outpatient clinic to the general practitioner: A pair-randomized controlled trial," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-13, October.
    20. Chin Lin & Hsiang-Cheng Chen & Wen-Hui Fang & Chih-Chien Wang & Yi-Jen Peng & Herng-Sheng Lee & Hung Chang & Chi-Ming Chu & Guo-Shu Huang & Wei-Teing Chen & Yu-Jui Tsai & Hong-Ling Lin & Fu-Huang Lin , 2016. "Angiotensin-Converting Enzyme Insertion/Deletion Polymorphism and Susceptibility to Osteoarthritis of the Knee: A Case-Control Study and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-18, September.
    21. Vize, Colin E. & Miller, Joshua D. & Lynam, Donald R., 2018. "FFM facets and their relations with different forms of antisocial behavior: An expanded meta-analysis," Journal of Criminal Justice, Elsevier, vol. 57(C), pages 67-75.
    22. Evangelos Danopoulos & Maureen Twiddy & Jeanette M Rotchell, 2020. "Microplastic contamination of drinking water: A systematic review," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-23, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0259121. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.