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Recurrent events analysis in the presence of time‐dependent covariates and dependent censoring

Author

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  • Maja Miloslavsky
  • Sündüz Keleş
  • Mark J. van der Laan
  • Steve Butler

Abstract

Summary. Recurrent events models have had considerable attention recently. The majority of approaches show the consistency of parameter estimates under the assumption that censoring is independent of the recurrent events process of interest conditional on the covariates that are included in the model. We provide an overview of available recurrent events analysis methods and present an inverse probability of censoring weighted estimator for the regression parameters in the Andersen–Gill model that is commonly used for recurrent event analysis. This estimator remains consistent under informative censoring if the censoring mechanism is estimated consistently, and it generally improves on the naïve estimator for the Andersen–Gill model in the case of independent censoring. We illustrate the bias of ad hoc estimators in the presence of informative censoring with a simulation study and provide a data analysis of recurrent lung exacerbations in cystic fibrosis patients when some patients are lost to follow‐up.

Suggested Citation

  • Maja Miloslavsky & Sündüz Keleş & Mark J. van der Laan & Steve Butler, 2004. "Recurrent events analysis in the presence of time‐dependent covariates and dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 239-257, February.
    • Handle: RePEc:bla:jorssb:v:66:y:2004:i:1:p:239-257
    DOI: 10.1111/j.1467-9868.2004.00442.x
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    Cited by:

    1. Ming‐Yueh Huang & Chiung‐Yu Huang, 2023. "Improved semiparametric estimation of the proportional rate model with recurrent event data," Biometrics, The International Biometric Society, vol. 79(3), pages 1686-1700, September.
    2. Jean‐Yves Dauxois & Sophie Sencey, 2009. "Non‐parametric Tests for Recurrent Events under Competing Risks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 649-670, December.
    3. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    4. Sharad Borle & Siddharth Singh & Dipak Jain & Ashutosh Patil, 2016. "Analyzing Recurrent Customer Purchases and Unobserved Defections: a Bayesian Data Augmentation Scheme," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(1), pages 11-28, March.
    5. Jin-Jian Hsieh & A. Adam Ding & Weijing Wang, 2011. "Regression Analysis for Recurrent Events Data under Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 719-729, September.
    6. Babykina, Génia & Couallier, Vincent, 2012. "Empirical assessment of the Maximum Likelihood Estimator quality in a parametric counting process model for recurrent events," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 297-315.
    7. Xiaoyu Che & John Angus, 2016. "A new joint model of recurrent event data with the additive hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(7), pages 763-787, October.
    8. Sharad Borle & Siddharth Shekhar Singh & Dipak C. Jain & Ashutosh Patil, 2016. "Analyzing Recurrent Customer Purchases and Unobserved Defections: a Bayesian Data Augmentation Scheme," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(1), pages 11-28, March.
    9. Sehee Kim & Douglas E. Schaubel & Keith P. McCullough, 2018. "A C†index for recurrent event data: Application to hospitalizations among dialysis patients," Biometrics, The International Biometric Society, vol. 74(2), pages 734-743, June.

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