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A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information

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  • R. Crouchley
  • R. B. Davies

Abstract

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Suggested Citation

  • R. Crouchley & R. B. Davies, 1999. "A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 331-347.
    • Handle: RePEc:bla:jorssa:v:162:y:1999:i:3:p:331-347
    DOI: 10.1111/1467-985X.00139
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    Citations

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    Cited by:

    1. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.
    2. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    3. Yu, Lei & Tyas, Suzanne L. & Snowdon, David A. & Kryscio, Richard J., 2009. "Effects of ignoring baseline on modeling transitions from intact cognition to dementia," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3334-3343, July.
    4. Maria Jose Murcia, 2021. "Progressive and Rational CSR as Catalysts of New Product Introductions," Journal of Business Ethics, Springer, vol. 174(3), pages 613-627, December.
    5. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    6. H. Zhang & Q. Yu & C. Feng & D. Gunzler & P. Wu & X. M. Tu, 2012. "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2067-2079, June.
    7. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    8. Thomas R. Ten Have & Beth A. Reboussin & Michael E. Miller & Allen Kunselman, 2002. "Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes," Biometrics, The International Biometric Society, vol. 58(1), pages 137-144, March.

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