Literature DB >> 21689077

A note on type II error under random effects misspecification in generalized linear mixed models.

John M Neuhaus1, Charles E McCulloch, Ross Boylan.   

Abstract

Litière, Alonso, and Molenberghs (2007, Biometrics, 63, 1038-1044) presented the results of simulation studies that they claimed showed that misspecification of the shape of the random effects distribution can produce marked increases in Type II error (decreases in power) of tests based on fits of generalized linear mixed models. However, the article contains a logical fallacy that invalidates this claim. We present logically correct simulation studies that demonstrate little increase in Type II error, consistent with the earlier work that shows little effect due to misspecification.
© 2010, The International Biometric Society.

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Year:  2011        PMID: 21689077      PMCID: PMC3079788          DOI: 10.1111/j.1541-0420.2010.01474.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  2 in total

1.  Random effects models with non-parametric priors.

Authors:  S M Butler; T A Louis
Journal:  Stat Med       Date:  1992 Oct-Nov       Impact factor: 2.373

2.  Type I and Type II error under random-effects misspecification in generalized linear mixed models.

Authors:  Saskia Litière; Ariel Alonso; Geert Molenberghs
Journal:  Biometrics       Date:  2007-04-09       Impact factor: 2.571

  2 in total
  8 in total

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6.  The effect of misspecification of random effects distributions in clustered data settings with outcome-dependent sampling.

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Journal:  Can J Stat       Date:  2011-07-27       Impact factor: 0.875

7.  Site-specific Solid Cancer Mortality After Exposure to Ionizing Radiation: A Cohort Study of Workers (INWORKS).

Authors:  David B Richardson; Elisabeth Cardis; Robert D Daniels; Michael Gillies; Richard Haylock; Klervi Leuraud; Dominique Laurier; Monika Moissonnier; Mary K Schubauer-Berigan; Isabelle Thierry-Chef; Ausrele Kesminiene
Journal:  Epidemiology       Date:  2018-01       Impact factor: 4.822

8.  Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics.

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Journal:  Stat Sci       Date:  2016-02-10       Impact factor: 2.901

  8 in total

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