Calsavara Vinicius F, Rodrigues Agatha S, Rocha Ricardo, Tomazella Vera, Louzada Francisco
Department of Epidemiology and Statistics, A.C. Camargo Cancer Center, São Paulo, SP, Brazil.
Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil.
Biom J. 2019 Jul;61(4):841-859. doi: 10.1002/bimj.201800056. Epub 2019 Mar 14.
Regression models in survival analysis are most commonly applied for right-censored survival data. In some situations, the time to the event is not exactly observed, although it is known that the event occurred between two observed times. In practice, the moment of observation is frequently taken as the event occurrence time, and the interval-censored mechanism is ignored. We present a cure rate defective model for interval-censored event-time data. The defective distribution is characterized by a density function whose integration assumes a value less than one when the parameter domain differs from the usual domain. We use the Gompertz and inverse Gaussian defective distributions to model data containing cured elements and estimate parameters using the maximum likelihood estimation procedure. We evaluate the performance of the proposed models using Monte Carlo simulation studies. Practical relevance of the models is illustrated by applying datasets on ovarian cancer recurrence and oral lesions in children after liver transplantation, both of which were derived from studies performed at A.C. Camargo Cancer Center in São Paulo, Brazil.
生存分析中的回归模型最常用于右删失生存数据。在某些情况下，尽管已知事件发生在两个观测时间之间，但事件发生的时间并未被精确观测到。在实际中，观测时刻常被当作事件发生时间，而区间删失机制被忽略。我们提出了一种针对区间删失事件时间数据的治愈率缺陷模型。缺陷分布由一个密度函数表征，当参数域与通常的域不同时，其积分值小于1。我们使用冈珀茨和逆高斯缺陷分布对包含治愈元素的数据进行建模，并使用最大似然估计程序估计参数。我们通过蒙特卡罗模拟研究评估所提出模型的性能。通过应用关于卵巢癌复发和肝移植后儿童口腔病变的数据集来说明模型的实际相关性，这两个数据集均来自巴西圣保罗的A.C.卡马戈癌症中心所开展的研究。
