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The random‐effects proportional hazards model with grouped survival data: a comparison between the grouped continuous and continuation ratio versions

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  • Leonardo Grilli

Abstract

Summary. When analysing grouped time survival data having a hierarchical structure it is often appropriate to assume a random‐effects proportional hazards model for the latent continuous time and then to derive the corresponding grouped time model. There are two formally equivalent grouped time versions of the proportional hazards model obtained from different perspec‐tives, known as the continuation ratio and the grouped continuous models. However, the two models require distinct estimation procedures and, more importantly, they differ substantially when extended to time‐dependent covariates and/or non‐proportional effects. The paper discusses these issues in the context of random‐effects models, illustrating the main points with an application to a complex data set on job opportunities for a cohort of graduates.

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  • Leonardo Grilli, 2005. "The random‐effects proportional hazards model with grouped survival data: a comparison between the grouped continuous and continuation ratio versions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 83-94, January.
    • Handle: RePEc:bla:jorssa:v:168:y:2005:i:1:p:83-94
    DOI: 10.1111/j.1467-985X.2004.00337.x
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    Cited by:

    1. Alexander Ahammer & Stefan Kranzinger, 2017. "Poverty in Times of Crisis," Economics working papers 2017-03, Department of Economics, Johannes Kepler University Linz, Austria.
    2. Guillaume Horny & Rute Mendes & Gerard J. van den Berg, 2012. "Job Durations With Worker- and Firm-Specific Effects: MCMC Estimation With Longitudinal Employer--Employee Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 468-480, March.
    3. Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    4. Ohinata, Asako, 2008. "Fertility Response to Financial Incentives-Evidence from the Working Families Tax Credit in the UK," The Warwick Economics Research Paper Series (TWERPS) 851, University of Warwick, Department of Economics.
    5. Guillaume Horny & Rute Mendes & Gerard J. Van den Berg, 2006. "Job mobility in Portugal: a Bayesian study with matched worker-firm data," Working Papers of BETA 2006-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Syden Mishi & Weliswa Matekenya & Leward Jeke & Ronney M. Ncwadi & Roseline T. Karambakuwa, 2021. "Firm and product survival analysis: Evidence from South African tax administrative and products data," WIDER Working Paper Series wp-2021-107, World Institute for Development Economic Research (UNU-WIDER).
    7. Ambrogi, Federico & Biganzoli, Elia & Boracchi, Patrizia, 2009. "Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2767-2779, May.
    8. Georgios Sermpinis & Serafeim Tsoukas & Ping Zhang, 2019. "What influences a bank's decision to go public?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1464-1485, October.

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