Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
Stat Methods Med Res. 2021 Jun;30(6):1484-1501. doi: 10.1177/09622802211002868. Epub 2021 Apr 19.
Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.
零膨胀计数和事件时间数据的联合建模通常通过应用共享随机效应模型来完成。这种联合建模可以被视为一个潜在的高斯模型。在本文中，我们使用集成嵌套拉普拉斯逼近（INLA）方法来对联合建模进行近似贝叶斯处理。我们提出了一个零膨胀障碍模型，假设计数数据服从泊松分布或负二项分布。同时，使用威布尔模型作为生存时间子模型。除了常用的联合线性模型外，还考虑了联合部分线性模型，以考虑时间对纵向计数响应的非线性影响。通过一些模拟研究来评估该方法的性能，并通过蒙特卡罗马尔可夫链（MCMC）的贝叶斯范例来比较其与常用方法的差异。此外，我们还将所提出的方法应用于分析两个真实数据集。第一个数据集是关于妊娠的纵向研究数据，第二个数据集是从 HIV 研究中获得的数据。
