Quantile regression model for interval-censored data with competing risks

Amirah Afiqah binti Che Ramli and Yang-Jin Kim

Journal of Applied Statistics, 2025, vol. 52, issue 13, 2438-2447

Abstract: Our interest is to provide the methodology for estimating quantile regression model for interval-censored competing risk data. Lee and Kim [Analysis of interval censored competing risk data via nonparametric multiple imputation. Stat. Biopharm. Res. 13 (2020), pp. 367–374.] applied a censoring complete data concept suggested by Ruan and Gray [Analyses of cumulative incidence function via non-parametric multiple imputation. Sta. Med. 27 (2008), pp. 5709–5724.] to recover a missing information related with competing events. In this paper, we also applied it to a quantile regression model. The simulated censoring times of the competing events are generated with a multiple imputation technique and the survival function of right censoring times. The performance of suggested methods is evaluated by comparing with the result of a simple imputation method under several distributions and sample sizes. The AIDS dataset is analyzed to estimate the effect of several covariates on the quantiles of cause-specific CIF as a real data analysis.

Date: 2025
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DOI: 10.1080/02664763.2025.2474627

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