Latency function estimation under the mixture cure model when the cure status is available.
Lifetime Data Anal
; 29(3): 608-627, 2023 07.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2279241
ABSTRACT
This paper addresses the problem of estimating the conditional survival function of the lifetime of the subjects experiencing the event (latency) in the mixture cure model when the cure status information is partially available. The approach of past work relies on the assumption that long-term survivors are unidentifiable because of right censoring. However, in some cases this assumption is invalid since some subjects are known to be cured, e.g., when a medical test ascertains that a disease has entirely disappeared after treatment. We propose a latency estimator that extends the nonparametric estimator studied in López-Cheda et al. (TEST 26(2)353-376, 2017b) to the case when the cure status is partially available. We establish the asymptotic normality distribution of the estimator, and illustrate its performance in a simulation study. Finally, the estimator is applied to a medical dataset to study the length of hospital stay of COVID-19 patients requiring intensive care.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
COVID-19
Tipo de estudio:
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
Lifetime Data Anal
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
S10985-023-09591-x
Similares
MEDLINE
...
LILACS
LIS