Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
Department of Biostatistics, University of Pittsburgh, PA, USA.
Stat Methods Med Res. 2023 Apr;32(4):656-670. doi: 10.1177/09622802221133552. Epub 2023 Feb 3.
We aim to evaluate the marginal effects of covariates on time-to-disability in the elderly under the semi-competing risks framework, as death dependently censors disability, not vice versa. It becomes particularly challenging when time-to-disability is subject to interval censoring due to intermittent assessments. A left truncation issue arises when the age time scale is applied. We develop a flexible two-parameter copula-based semiparametric transformation model for semi-competing risks data subject to interval censoring and left truncation. The two-parameter copula quantifies both upper and lower tail dependence between two margins. The semiparametric transformation models incorporate proportional hazards and proportional odds models in both margins. We propose a two-step sieve maximum likelihood estimation procedure and study the sieve estimators' asymptotic properties. Simulations show that the proposed method corrects biases in the marginal method. We demonstrate the proposed method in a large-scale Chinese Longitudinal Healthy Longevity Study and provide new insights into preventing disability in the elderly. The proposed method could be applied to the general semi-competing risks data with intermittently assessed disease status.
我们旨在评估半竞争风险框架下老年人残疾时间的协变量边际效应，因为残疾依赖于死亡进行删失，而不是相反。当残疾时间因间歇性评估而受到区间删失时，这变得特别具有挑战性。当应用年龄时间尺度时，会出现左截断问题。我们为受到区间删失和左截断的半竞争风险数据开发了一个灵活的两参数 Copula 基于半参数转换模型。两参数 Copula 量化了两个边缘之间的上尾和下尾依赖性。半参数转换模型在两个边缘中都包含比例风险和比例优势模型。我们提出了一种两步筛最大似然估计程序，并研究了筛估计器的渐近性质。模拟表明，所提出的方法纠正了边际方法的偏差。我们在中国大规模的纵向健康长寿研究中展示了该方法，并为预防老年人残疾提供了新的见解。所提出的方法可以应用于一般的半竞争风险数据，其中疾病状态间歇性评估。
