Sildnes Beate, Lindqvist Bo Henry
Department of Mathematical Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
BearingPoint, Tjuvholmen allé 3, 0252, Oslo, Norway.
Lifetime Data Anal. 2018 Jan;24(1):153-175. doi: 10.1007/s10985-017-9399-y. Epub 2017 Jul 22.
In semi-competing risks one considers a terminal event, such as death of a person, and a non-terminal event, such as disease recurrence. We present a model where the time to the terminal event is the first passage time to a fixed level c in a stochastic process, while the time to the non-terminal event is represented by the first passage time of the same process to a stochastic threshold S, assumed to be independent of the stochastic process. In order to be explicit, we let the stochastic process be a gamma process, but other processes with independent increments may alternatively be used. For semi-competing risks this appears to be a new modeling approach, being an alternative to traditional approaches based on illness-death models and copula models. In this paper we consider a fully parametric approach. The likelihood function is derived and statistical inference in the model is illustrated on both simulated and real data.
在半竞争风险模型中,我们考虑一个终端事件,比如人的死亡,以及一个非终端事件,比如疾病复发。我们提出了一个模型,其中终端事件的发生时间是随机过程首次达到固定水平c的时间,而非终端事件的发生时间则由同一过程首次达到随机阈值S的时间表示,假设该阈值与随机过程相互独立。为了明确起见,我们令随机过程为伽马过程,但也可以使用其他具有独立增量的过程。对于半竞争风险而言,这似乎是一种新的建模方法,是基于疾病-死亡模型和copula模型的传统方法的替代方案。在本文中,我们考虑一种完全参数化方法。推导了似然函数,并通过模拟数据和实际数据说明了模型中的统计推断。