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Articles

Use of between-within degrees of freedom as an alternative to the Kenward–Roger method for small-sample inference in generalized linear mixed modeling of clustered count data

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Pages 5099-5109 | Received 06 Jul 2021, Accepted 14 Sep 2021, Published online: 30 Sep 2021

References

  • Bell, M. L., and G. K. Grunwald. 2011. Small sample estimation properties of longitudinal count models. Journal of Statistical Computation and Simulation. 81 (9):1067–79. doi:10.1080/00949651003674144.
  • Demidenko, E. 2004. Mixed models: Theory and applications. Hoboken, NJ: John Wiley & Sons.
  • Durán, P. G., J. Hattendorf, J. M. Colford, Jr, D. Mäusezahl, and T. Smith. 2009. Performance of analytical methods for overdispersed counts in cluster randomized trials: Sample size, degree of clustering and imbalance. Statistics in Medicine 28 (24):2989–3011. doi:10.1002/sim.3681.
  • Fialkowski, A., and H. Tiwari. 2019. SimCorrMix: Simulation of correlated data with multiple variable types including continuous and count mixture distributions. The R Journal 11 (1):250–86. doi:10.32614/RJ-2019-022.
  • Francq, B. G., D. Lin, and W. Hoyer. 2019. Confidence, prediction, and tolerance in linear mixed models. Statistics in Medicine 38 (30):5603–22. doi:10.1002/sim.8386.
  • Jackson, C. L., K. Colborn, D. Gao, S. Rao, H. C. Slater, S. Parikh, B. D. Foy, and J. Kittelson. 2021. Design and analysis of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria: Small sample considerations for cluster-randomized trials with count data. Clinical Trials (London, England). Advance online publication. doi:10.1177/17407745211028581.
  • Kalema, G., and G. Molenberghs. 2016. Generating correlated and/or overdispersed count data: A SAS implementation. J Stat Softw 70 (Code Snippet 1). Advance online publication. doi:10.18637/jss.v070.c01.
  • Kenward, M. G., and J. H. Roger. 1997. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53 (3):983–97.
  • Kenward, M. G., and J. H. Roger. 2009. An improved approximation to the precision of fixed effects from restricted maximum likelihood. Computational Statistics and Data Analysis. 53 (7):2583–95. doi:10.1016/j.csda.2008.12.013.
  • Li, P., and D. T. Redden. 2015. Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Medical Research Methodology 15 (1):38. doi:10.1186/s12874-015-0026-x.
  • Littell, R. C. 2002. Analysis of unbalanced mixed model data: A case study comparison of ANOVA versus REML/GLS. Journal of Agricultural, Biological, and Environmental Statistics 7 (4):472–90. doi:10.1198/108571102816.
  • Luke, S. G. 2017. Evaluating significance in linear mixed-effects models in R. Behavior Research Methods 49 (4):1494–502. doi:10.3758/s13428-016-0809-y.
  • McNeish, D. 2019. Poisson multilevel models with small samples. Multivariate Behavioral Research 54 (3):444–55. doi:10.1080/00273171.2018.1545630.
  • McNeish, D., and L. M. Stapleton. 2016. Modeling clustered data with very few clusters. Multivariate Behavioral Research 51 (4):495–518. doi:10.1080/00273171.2016.1167008.
  • Schaalje, G. B., J. B. McBride, and G. W. Fellingham. 2002. Adequacy of approximations to distributions of test statistics in complex mixed linear models. Journal of Agricultural, Biological, and Environmental Statistics 7 (4):512–24. doi:10.1198/108571102726.
  • Schluchter, M. D., and J. T. Elashoff. 1990. Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures. Journal of Statistical Computation and Simulation 37 (1-2):69–87. doi:10.1080/00949659008811295.
  • Staggs, V. S. 2017. Comparison of naïve, Kenward–Roger, and parametric bootstrap interval approaches to small-sample inference in linear mixed models. Communications in Statistics - Simulation and Computation 46 (3):1933–43. doi:10.1080/03610918.2015.1019002.
  • Stroup, W. W. 2015. Rethinking the analysis of non‐normal data in plant and soil science. Agronomy Journal. 107 (2):811–27. doi:10.2134/agronj2013.0342.
  • Stroup, W. W. 2018. Non-normal data in agricultural experiments. Conf. Appl. Stat. Agric. doi:10.4148/2475-7772.1018.

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