ArticleLifetime data analysis2023
Latency function estimation under the mixture cure model when the cure status is available.
Article in Lifetime data analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Covariate hypothesis tests for the cure rate in mixture cure models based on martingale difference correlation.Statistical methods in medical research · 2026Article
- Fitting high-dimensional mixture cure models using the hdcuremodelsR package.Computer methods and programs in biomedicine · 2026Article
- A new cure model accounting for longitudinal data and flexible patterns of hazard ratios over time.Statistical methods in medical research · 2025Article
- Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.PloS one · 2023Article
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3 authors.
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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.
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