Research article Special Issues

Predicting disease risks by matching quantiles estimation for censored data

  • Received: 29 March 2020 Accepted: 18 June 2020 Published: 29 June 2020
  • In time to event data analysis, it is often of interest to predict quantities such as t-year survival rate or the survival function over a continuum of time. A commonly used approach is to relate the survival time to the covariates by a semiparametric regression model and then use the fitted model for prediction, which usually results in direct estimation of the conditional hazard function or the conditional estimating equation. Its prediction accuracy, however, relies on the correct specification of the covariate-survival association which is often difficult in practice, especially when patient populations are heterogeneous or the underlying model is complex. In this paper, from a prediction perspective, we propose a disease-risk prediction approach by matching an optimal combination of covariates with the survival time in terms of distribution quantiles. The proposed method is easy to implement and works flexibly without assuming a priori model. The redistribution-of-mass technique is adopted to accommodate censoring. We establish theoretical properties of the proposed method. Simulation studies and a real data example are also provided to further illustrate its practical utilities.

    Citation: Peng Wu, Baosheng Liang, Yifan Xia, Xingwei Tong. Predicting disease risks by matching quantiles estimation for censored data[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4544-4562. doi: 10.3934/mbe.2020251

    Related Papers:

  • In time to event data analysis, it is often of interest to predict quantities such as t-year survival rate or the survival function over a continuum of time. A commonly used approach is to relate the survival time to the covariates by a semiparametric regression model and then use the fitted model for prediction, which usually results in direct estimation of the conditional hazard function or the conditional estimating equation. Its prediction accuracy, however, relies on the correct specification of the covariate-survival association which is often difficult in practice, especially when patient populations are heterogeneous or the underlying model is complex. In this paper, from a prediction perspective, we propose a disease-risk prediction approach by matching an optimal combination of covariates with the survival time in terms of distribution quantiles. The proposed method is easy to implement and works flexibly without assuming a priori model. The redistribution-of-mass technique is adopted to accommodate censoring. We establish theoretical properties of the proposed method. Simulation studies and a real data example are also provided to further illustrate its practical utilities.
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    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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