most citedNonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models

70 citations · 173 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME202422 cited

Cure models to estimate time until hospitalization due to COVID-19

Maria Pedrosa-Laza, Ana López-Cheda, Ricardo Cao

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the mo…

stat.ME202414 cited

npcure: An R Package for Nonparametric Inference in Mixture Cure Models

Ana López-Cheda, M. Amalia Jácome, Ignacio López-de-Ullibarri

Mixture cure models have been widely used to analyze survival data with a cure fraction. They assume that a subgroup of the individuals under study will never experience the event…

stat.ME202470 cited

Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models

Ana López-Cheda, Ricardo Cao, M. Amalia Jácome +1

A completely nonparametric method for the estimation of mixture cure models is proposed. A nonparametric estimator of the incidence is extensively studied and a nonparametric estim…

stat.ME202419 cited

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models

Ana López-Cheda, M. Amalia Jácome, Ingrid Van Keilegom +1

In lifetime data, like cancer studies, theremay be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not app…

stat.ME202436 cited

Nonparametric latency estimation for mixture cure models

Ana López-Cheda, M. Amalia Jácome, Ricardo Cao

A nonparametric latency estimator for mixture cure models is studied in this paper. An i.i.d. representation is obtained, the asymptotic mean squared error of the latency estimator…

stat.ME202412 cited

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

Ana López-Cheda, M. Amalia Jácome, Ricardo Cao +1

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healt…