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Articles

Fast two-stage estimator for clustered count data with overdispersion

ORCID Icon, ORCID Icon, , , &
Pages 2678-2693 | Received 09 Jan 2019, Accepted 06 Jun 2019, Published online: 19 Jun 2019

References

  • Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc. 1993;88(421):9–25.
  • Molenberghs G, Verbeke G. Models for discrete longitudinal data. New-York (NY): Springer; 2005.
  • McCullagh P, Nelder JA. Generalized linear models. London: Chapman & Hall / CRC; 1989.
  • Laird N, Ware J. Random-effects models for longitudinal data. Biometrics. 1983;38(4):963–974. doi: 10.2307/2529876
  • Verbeke G, Molenberghs G. Linear mixed models for longitudinal data. New York (NY): Springer; 2000.
  • Lawless JF. Negative binomial and mixed poisson regression. Can J Stat. 1987;15(3):209–225. doi: 10.2307/3314912
  • Booth JG, Casella G, Friedl H, et al. Negative binomial loglinear mixed models. Stat Model. 2003;3(3):179–191. doi: 10.1191/1471082X03st058oa
  • Molenberghs G, Verbeke G, Demétrio CGB, et al. A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25(3):325–347. doi: 10.1214/10-STS328
  • Molenberghs G, Verbeke G, Iddi S. Pseudo-likelihood methodology for partitioned large and complex samples. Stat Probab Lett. 2011;81(7):892–901. doi: 10.1016/j.spl.2011.01.012
  • Ivanova A, Molenberghs G, Verbeke G. Fast and highly efficient pseudo-likelihood methodology for large and complex ordinal data. Stat Methods Med Res. 2015;26(6):2758–2779. doi: 10.1177/0962280215608213
  • Hermans L, Nassiri V, Molenberghs G, et al. Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes. Commun Stat-Simul C. 2018;47(5):1492–1505. doi: 10.1080/03610918.2017.1316395
  • Molenberghs G, Hermans L, Nassiri V, et al. Clusters with random size: maximum likelihood versus weighted estimation. Stat Sin. 2018;28(3):1107–1132.
  • Hermans L, Molenberghs G, Aerts M, et al. A tutorial on the practical use and implication of complete sufficient statistics. Int Stat Rev. 2018;86(3):403-–414. doi: 10.1111/insr.12261
  • Flórez AJ, Molenberghs G, Verbeke G, et al. A closed-form estimator for meta-analysis and surrogate markers evaluation. J Biopharm Stat. 2019;29(2):318-–332. doi: 10.1080/10543406.2018.1535504
  • Molenberghs G, Verbeke G, Demétrio CGB. An extended random-effects approach to modeling repeated, overdispersed count data. Lifetime Data Anal. 2007;13(4):513–531. doi: 10.1007/s10985-007-9064-y
  • Truyers C, Goderis G, Dewitte H, et al. The intego database: background, methods and basic results of a flemish general practice-based continuous morbidity registration project. BMC Med Inform Decis Mak. 2014;14(1):48–57. doi: 10.1186/1472-6947-14-48
  • Rubin DB. Multiple imputation for nonresponse in surveys. New York (NY): Wiley; 1987.
  • van Buuren S. Flexible imputation of missing data. Boca Ratón (FL): Chapman and Hall/CRC; 2012.
  • Carpenter J, Kenward M. Multiple imputation and its application. Chichester, UK: Wiley; 2013.
  • Tuerlinckx F, Rijmen F, Verbeke G, et al. Statistical inference in generalized linear mixed models: A review. Br J Math Stat Psychol. 2006;59(2):225–255. doi: 10.1348/000711005X79857
  • Cameron AC, Trivedi P. Regression analysis of count data. Cambridge, UK: Cambridge University Press; 2013.
  • R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018.
  • SAS Institute. The SAS system for Windows release 9.4. Cary (NC): SAS Institute; 2011.
  • van Buuren S, Brand J, Groothuis-Oudshoorn C. et al. Fully conditional specification in multivariate imputation. J Stat Comput Simul. 2006;76(12):1049–1064. doi: 10.1080/10629360600810434
  • van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1-–67.
  • Little RJA, Rubin DB. Statistical analysis with missing data. Hoboken (NJ): Wiley; 2002.
  • Ridout M, Demétrio CGB, Hinde J. Models for count data with many zeros. International Biometric Conference XIX; 1998; Cape Town, South Africa; p. 179–192.
  • Fieuws S, Verbeke G. Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics. 2006;62(2):424–431. doi: 10.1111/j.1541-0420.2006.00507.x
  • Fieuws S, Verbeke G, Boen F, et al. High dimensional multivariate mixed models for binary questionnaire data. J R Stat Soc C-Appl. 2006;55(4):449–460. doi: 10.1111/j.1467-9876.2006.00546.x

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