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Methodological Quality and Reporting of Generalized Linear Mixed Models in Clinical Medicine (2000–2012): A Systematic Review

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  • Martí Casals
  • Montserrat Girabent-Farrés
  • Josep L Carrasco

Abstract

Background: Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. Methods: A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic “generalized linear mixed models”,“hierarchical generalized linear models”, “multilevel generalized linear model” and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. Results: A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. Conclusions: During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.

Suggested Citation

  • Martí Casals & Montserrat Girabent-Farrés & Josep L Carrasco, 2014. "Methodological Quality and Reporting of Generalized Linear Mixed Models in Clinical Medicine (2000–2012): A Systematic Review," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
  • Handle: RePEc:plo:pone00:0112653
    DOI: 10.1371/journal.pone.0112653
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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
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    1. Anders Wimo & Katrin Seeher & Rodrigo Cataldi & Eva Cyhlarova & Joseph L Dielemann & Oskar Frisell & Maëlenn Guerchet & Linus Jönsson & Angeladine Kenne Malaha & Emma Nichols & Paola Pedroza & Martin , 2023. "The worldwide costs of dementia in 2019," Post-Print ird-03944615, HAL.
    2. Wimo, Anders & Seeher, Katrin & Cataldi, Rodrigo & Cyhlarova, Eva & Dielemann, Joseph L. & Frisell, Oskar & Guerchet, Maëlenn & Jönsson, Linus & Malaha, Angeladine Kenne & Nichols, Emma & Pedroza, Pao, 2023. "The worldwide costs of dementia in 2019," LSE Research Online Documents on Economics 118062, London School of Economics and Political Science, LSE Library.
    3. Nikki McCaffrey & Seong Leang Cheah & Tim Luckett & Jane L Phillips & Meera Agar & Patricia M Davidson & Frances Boyle & Tim Shaw & David C Currow & Melanie Lovell, 2023. "Treatment patterns and out-of-hospital healthcare resource utilisation by patients with advanced cancer living with pain: An analysis from the Stop Cancer PAIN trial," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-21, February.
    4. Heidi Seibold & Severin Czerny & Siona Decke & Roman Dieterle & Thomas Eder & Steffen Fohr & Nico Hahn & Rabea Hartmann & Christoph Heindl & Philipp Kopper & Dario Lepke & Verena Loidl & Maximilian Ma, 2021. "A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
    5. Byron C. Jaeger & Lloyd J. Edwards & Kalyan Das & Pranab K. Sen, 2017. "An statistic for fixed effects in the generalized linear mixed model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(6), pages 1086-1105, April.

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