Department of Epidemiology and Biostatistics, 3078Michigan State University, East Lansing, MI, USA.
Stat Methods Med Res. 2021 Mar;30(3):769-784. doi: 10.1177/0962280220975064. Epub 2020 Dec 1.
We develop a joint modeling method for multivariate interval-censored survival data and a time-dependent covariate that is intermittently measured with error. The joint model is estimated using nonparametric maximum likelihood estimation, which is carried out via an expectation-maximization algorithm, and the inference for finite-dimensional parameters is performed using bootstrap. We also develop a similar joint modeling method for univariate interval-censored survival data and a time-dependent covariate, which excels the existing methods in terms of model flexibility and interpretation. Simulation studies show that the model fitting and inference approaches perform very well under realistic sample sizes. We apply the method to a longitudinal study of dental caries in African-American children from low-income families in the city of Detroit, Michigan.
我们开发了一种用于多元区间 censored 生存数据和具有误差的间断性测量的时变协变量的联合建模方法。联合模型使用非参数最大似然估计进行估计，该方法通过期望最大化算法进行，并且使用引导进行有限维参数的推断。我们还开发了一种用于单变量区间 censored 生存数据和时变协变量的类似联合建模方法，该方法在模型灵活性和解释方面优于现有方法。模拟研究表明，在实际样本量下，模型拟合和推断方法的性能非常出色。我们将该方法应用于密歇根州底特律市低收入家庭的非裔美国儿童的龋齿纵向研究。
