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Methods for multivariate recurrent event data with measurement error and informative censoring

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  • Hsiang Yu
  • Yu‐Jen Cheng
  • Ching‐Yun Wang

Abstract

In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, we develop two non‐parametric correction approaches to simultaneously correct for the informative censoring and measurement errors in the analysis of multivariate recurrent event data. A shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a non‐parametric correction method using the calibration sample only is proposed. In the second approach, the information from the whole cohort is incorporated by the generalized method of moments. The proposed methods do not require the Poisson‐type assumption for the multivariate recurrent event process and the distributional assumption for the frailty. Moreover, we do not need to impose any distributional assumption on the underlying covariates and measurement error. Both methods perform well, but the second approach improves efficiency. The proposed methods are applied to the Nutritional Prevention of Cancer trial to assess the effect of selenium treatment on the recurrences of basal cell carcinoma and squamous cell carcinoma.

Suggested Citation

  • Hsiang Yu & Yu‐Jen Cheng & Ching‐Yun Wang, 2018. "Methods for multivariate recurrent event data with measurement error and informative censoring," Biometrics, The International Biometric Society, vol. 74(3), pages 966-976, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:966-976
    DOI: 10.1111/biom.12857
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    References listed on IDEAS

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    1. Gongjun Xu & Sy Han Chiou & Chiung-Yu Huang & Mei-Cheng Wang & Jun Yan, 2017. "Joint Scale-Change Models for Recurrent Events and Failure Time," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 794-805, April.
    2. Xiao Song & Ching-Yun Wang, 2014. "Proportional Hazards Model With Covariate Measurement Error and Instrumental Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1636-1646, December.
    3. John D. Kalbfleisch & Douglas E. Schaubel & Yining Ye & Qi Gong, 2013. "An Estimating Function Approach to the Analysis of Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 69(2), pages 366-374, June.
    4. Hu, Chengcheng & Lin, D.Y., 2004. "Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 105-118, January.
    5. Bedair, Khaled & Hong, Yili & Li, Jie & Al-Khalidi, Hussein R., 2016. "Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 161-173.
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    2. Xiaowei Sun & Jieli Ding & Liuquan Sun, 2020. "A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 471-492, July.

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