Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Association measure of doubly interval censored data using a Kendall's 𝜏 estimator

  • Received : 2020.07.28
  • Accepted : 2021.01.11
  • Published : 2021.03.31
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Abstract

In this article, our interest is to estimate the association between consecutive gap times which are subject to interval censoring. Such data are referred as doubly interval censored data (Sun, 2006). In a context of serial event, an induced dependent censoring frequently occurs, resulting in biased estimates. In this study, our goal is to propose a Kendall's 𝜏 based association measure for doubly interval censored data. For adjusting the impact of induced dependent censoring, the inverse probability censoring weighting (IPCW) technique is implemented. Furthermore, a multiple imputation technique is applied to recover unknown failure times owing to interval censoring. Simulation studies demonstrate that the suggested association estimator performs well with moderate sample sizes. The proposed method is applied to a dataset of children's dental records.

Keywords

Acknowledgement

This work was supported by a Korea Research Grant (NRF-2017R1D1A1B03030578).

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