Inequalities in Cancer Outcomes Network (ICON), Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Department of Mathematics, Escuela Universitaria Politécnica, University of A Coruña, Ferrol, Spain.
Lifetime Data Anal. 2023 Jul;29(3):608-627. doi: 10.1007/s10985-023-09591-x. Epub 2023 Mar 8.
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.
本文针对混合治愈模型中，当治愈状态信息部分可用时，估计经历事件（潜伏期）的受试者寿命的条件生存函数的问题。过去工作的方法依赖于一个假设，即由于右删失，长期幸存者无法识别。然而，在某些情况下，这种假设是无效的，因为有些受试者已经被治愈，例如，当医疗测试确定疾病在治疗后完全消失时。我们提出了一种潜伏期估计器，将 López-Cheda 等人在 TEST 26(2):353-376, 2017b 中研究的非参数估计器扩展到治愈状态部分可用的情况。我们建立了估计器的渐近正态分布，并在模拟研究中说明了其性能。最后，该估计器应用于一个医疗数据集，以研究需要重症监护的 COVID-19 患者的住院时间。
