Association measure of doubly interval censored data using a Kendall's tau estimator
- Authors
- Kang, Seo-Hyun; Kim, Yang-Jin
- Issue Date
- Mar-2021
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- doubly interval censored data; Kendall's tau; induced dependent censoring; IPCW; multiple imputation
- Citation
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.28, no.2, pp 151 - 159
- Pages
- 9
- Journal Title
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
- Volume
- 28
- Number
- 2
- Start Page
- 151
- End Page
- 159
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146721
- DOI
- 10.29220/CSAM.2021.28.2.151
- ISSN
- 2287-7843
2383-4757
- 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 tau 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.
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