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Random effects Cox models: A Poisson modelling approach

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  • Renjun Ma

Abstract

We propose a Poisson modelling approach to nested random effects Cox proportional hazards models. An important feature of this approach is that the principal results depend only on the first and second moments of the unobserved random effects. The orthodox best linear unbiased predictor approach to random effects Poisson modelling techniques enables us to justify appropriate consistency and optimality. The explicit expressions for the random effects given by our approach facilitate incorporation of a relatively large number of random effects. The use of the proposed methods is illustrated through the reanalysis of data from a large-scale cohort study of particulate air pollution and mortality previously reported by Pope et al. (1995). Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • Renjun Ma, 2003. "Random effects Cox models: A Poisson modelling approach," Biometrika, Biometrika Trust, vol. 90(1), pages 157-169, March.
    • Handle: RePEc:oup:biomet:v:90:y:2003:i:1:p:157-169
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    Citations

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    Cited by:

    1. Gifford, Elizabeth J. & Wells, Rebecca S. & Bai, Yu & Malone, Patrick S., 2015. "Is implementation fidelity associated with improved access to care in a School-based Child and Family Team model?," Evaluation and Program Planning, Elsevier, vol. 49(C), pages 41-49.
    2. Shih, Joanna H. & Lu, Shou-En, 2009. "Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3864-3871, September.
    3. Joanna H. Shih & Shou-En Lu, 2007. "Analysis of Failure Time Data with Multilevel Clustering, with Application to the Child Vitamin A Intervention Trial in Nepal," Biometrics, The International Biometric Society, vol. 63(3), pages 673-680, September.
    4. Tiago R. Pellegrini & M. Tariqul Hasan & Renjun Ma, 2017. "Modeling of paired zero-inflated continuous data without breaking down paired designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2427-2443, October.
    5. Mirza Nazmul Hasan & Roel Braekers, 2021. "Estimation of the association parameters in hierarchically clustered survival data by nested Archimedean copula functions," Computational Statistics, Springer, vol. 36(4), pages 2755-2787, December.
    6. M. Tariqul Hasan & Gary Sneddon & Renjun Ma, 2012. "Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 467-476, June.
    7. Courgeau, Daniel, 2007. "Multilevel synthesis. From the group to the individual," MPRA Paper 43189, University Library of Munich, Germany.
    8. Ying Hung & Li‐Hsiang Lin & C. F. Jeff Wu, 2022. "Varying coefficient frailty models with applications in single molecular experiments," Biometrics, The International Biometric Society, vol. 78(2), pages 474-486, June.
    9. Abrahantes, Jose Cortinas & Legrand, Catherine & Burzykowski, Tomasz & Janssen, Paul & Ducrocq, Vincent & Duchateau, Luc, 2007. "Comparison of different estimation procedures for proportional hazards model with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3913-3930, May.

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