A quantile regression estimator for interval-censored data

Frumento Paolo ()
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Frumento Paolo: University of Pisa, Pisa, Italy

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 81-96

Abstract: We describe an estimating equation that can be used to fit quantile regression models to interval-censored data. The proposed estimator presents important advantages over the existing methods, and can be applied when the data are a mixture of interval-censored, left-censored, and right-censored observations. We describe estimation and inference, report simulation results, and apply the proposed method to analyze the Signal Tandmobiel® data. The necessary R code has been incorporated in the existing R package c t q r $\mathtt{c}\mathtt{t}\mathtt{q}\mathtt{r}$ .

Keywords: interval-censored quantile regression; R package ctqr; signal Tandmobiel®data; two-step estimation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2021-0063

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