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Nonparametric estimation of the mean function for recurrent event data with missing event category

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  • Feng-Chang Lin
  • Jianwen Cai
  • Jason P. Fine
  • Huichuan J. Lai

Abstract

Recurrent event data frequently arise in longitudinal studies when study subjects possibly experience more than one event during the observation period. Often, such recurrent events can be categorized. However, part of the categorization may be missing due to technical difficulties. If the event types are missing completely at random, then a complete case analysis may provide consistent estimates of regression parameters in certain regression models, but estimates of the baseline event rates are generally biased. Previous work on nonparametric estimation of these rates has utilized parametric missingness models. In this paper, we develop fully nonparametric methods in which the missingness mechanism is completely unspecified. Consistency and asymptotic normality of the nonparametric estimators of the mean event functions accommodate nonparametric estimators of the event category probabilities, which converge more slowly than the parametric rate. Plug-in variance estimators are provided and perform well in simulation studies, where complete case estimators may exhibit large biases and parametric estimators generally have a larger mean squared error when the model is misspecified. The proposed methods are applied to data from a cystic fibrosis registry. Copyright 2013, Oxford University Press.

Suggested Citation

  • Feng-Chang Lin & Jianwen Cai & Jason P. Fine & Huichuan J. Lai, 2013. "Nonparametric estimation of the mean function for recurrent event data with missing event category," Biometrika, Biometrika Trust, vol. 100(3), pages 727-740.
  • Handle: RePEc:oup:biomet:v:100:y:2013:i:3:p:727-740
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    File URL: http://hdl.handle.net/10.1093/biomet/ast016
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    Cited by:

    1. Ye, Peng & Zhao, Xingqiu & Sun, Liuquan & Xu, Wei, 2015. "A semiparametric additive rates model for multivariate recurrent events with missing event categories," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 39-50.
    2. Natalia A. Gouskova & Feng-Chang Lin & Jason P. Fine, 2017. "Nonparametric analysis of competing risks data with event category missing at random," Biometrics, The International Biometric Society, vol. 73(1), pages 104-113, March.
    3. Huijuan Ma & Limin Peng & Zhumin Zhang & HuiChuan J. Lai, 2018. "Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type," Biometrics, The International Biometric Society, vol. 74(3), pages 954-965, September.

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