Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times

• Ewa Strzalkowska-Kominiak ^[1] ; Elisa M. Molanes-López ^[2] ; Emilio Letón ^[3]
  1. [1] Universidad Carlos III de Madrid

     Universidad Carlos III de Madrid

     Madrid, España

     
  2. [2] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
  3. [3] Universidad Nacional de Educación a Distancia

     Universidad Nacional de Educación a Distancia

     Madrid, España

     

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• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 48, Nº. 2, 2024, págs. 183-208
• Idioma: inglés
• Enlaces
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• Resumen

  • In many biomedical studies, recurrent or consecutive events may occur during the follow up of the individuals. This situation can be found, for example, in transplant studies, where there are two consecutive events which give rise to two times of interest subject to a common random right-censoring time, the first one being the elapsed time from acceptance into the transplantation program to transplant, and the second one the time from transplant to death. In this work, we incorporate the information of a continuous covariate into the bivariate distribution of the two gap times of interest and propose a non-parametric method to cope with it. We prove the asymptotic properties of the proposed method and carry out a simulation study to see the performance of this approach. Additionally, we illustrate its use with Stanford heart transplant data and colon cancer data.

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