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Semiparametric latent class analysis of recurrent event data

Author

Listed:
  • Wei Zhao
  • Limin Peng
  • John Hanfelt

Abstract

Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. In this work, we investigate latent class analysis of recurrent event data based on flexible semiparametric multiplicative modelling. We derive a robust estimation procedure through novelly adapting the conditional score technique and utilizing the special characteristics of multiplicative intensity modelling. The proposed estimation procedure can be stably and efficiently implemented based on existing computational routines. We provide solid theoretical underpinnings for the proposed method, and demonstrate its satisfactory finite sample performance via extensive simulation studies. An application to a dataset from research participants at Goizueta Alzheimer's Disease Research Center illustrates the practical utility of our proposals.

Suggested Citation

  • Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.
    • Handle: RePEc:bla:jorssb:v:84:y:2022:i:4:p:1175-1197
    DOI: 10.1111/rssb.12499
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    References listed on IDEAS

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    1. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    2. Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
    3. Donglin Zeng & Lu Mao & D. Y. Lin, 2016. "Maximum likelihood estimation for semiparametric transformation models with interval-censored data," Biometrika, Biometrika Trust, vol. 103(2), pages 253-271.
    4. Xianghua Luo & Chiung-Yu Huang & Lan Wang, 2013. "Quantile Regression for Recurrent Gap Time Data," Biometrics, The International Biometric Society, vol. 69(2), pages 375-385, June.
    5. Ross P. Hilton & Yuchen Zheng & Nicoleta Serban, 2018. "Modeling Heterogeneity in Healthcare Utilization Using Massive Medical Claims Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 111-121, January.
    6. Haiqun Lin & Charles E. McCulloch & Robert A. Rosenheck, 2004. "Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 60(2), pages 295-305, June.
    7. Dongbing Lai & Huiping Xu & Daniel Koller & Tatiana Foroud & Sujuan Gao, 2016. "A multivariate finite mixture latent trajectory model with application to dementia studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2503-2523, October.
    8. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
    9. Han, Jun, 2009. "Initial classification of joint data in EM estimation of latent class joint model," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2313-2323, November.
    10. Wedel, M, et al, 1993. "A Latent Class Poisson Regression Model for Heterogeneous Count Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 397-411, Oct.-Dec..
    11. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    12. Lin H. & Turnbull B. W. & McCulloch C. E. & Slate E. H., 2002. "Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data: Application to Longitudinal Prostate-Specific Antigen Readings and Prostate Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 53-65, March.
    13. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    14. Brian L. Egleston & Robert G. Uzzo & Yu-Ning Wong, 2017. "Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 534-546, April.
    15. Patrick Mair & Marcus Hudec, 2009. "Multivariate Weibull mixtures with proportional hazard restrictions for dwell‐time‐based session clustering with incomplete data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 619-639, December.
    16. Donglin Zeng & Fei Gao & D. Y. Lin, 2017. "Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data," Biometrika, Biometrika Trust, vol. 104(3), pages 505-525.
    17. Kamel Jedidi & Venkatram Ramaswamy & Wayne Desarbo, 1993. "A maximum likelihood method for latent class regression involving a censored dependent variable," Psychometrika, Springer;The Psychometric Society, vol. 58(3), pages 375-394, September.
    18. Lavancier, F. & Rochet, P., 2016. "A general procedure to combine estimators," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 175-192.
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