Baghfalaki T, Ganjali M, Kabir A, Pazouki A
Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
J Appl Stat. 2020 Sep 18;49(3):638-655. doi: 10.1080/02664763.2020.1822303. eCollection 2022.
Joint modeling of associated mixed biomarkers in longitudinal studies leads to a better clinical decision by improving the efficiency of parameter estimates. In many clinical studies, the observed time for two biomarkers may not be equivalent and one of the longitudinal responses may have recorded in a longer time than the other one. In addition, the response variables may have different missing patterns. In this paper, we propose a new joint model of associated continuous and binary responses by accounting different missing patterns for two longitudinal outcomes. A conditional model for joint modeling of the two responses is used and two shared random effects models are considered for intermittent missingness of two responses. A Bayesian approach using Markov Chain Monte Carlo (MCMC) is adopted for parameter estimation and model implementation. The validation and performance of the proposed model are investigated using some simulation studies. The proposed model is also applied for analyzing a real data set of bariatric surgery.
纵向研究中相关混合生物标志物的联合建模通过提高参数估计效率带来更好的临床决策。在许多临床研究中，两种生物标志物的观察时间可能不等同，且其中一个纵向反应的记录时间可能比另一个更长。此外，反应变量可能具有不同的缺失模式。在本文中，我们通过考虑两个纵向结果的不同缺失模式，提出了一种新的相关连续和二元反应的联合模型。使用一个用于两个反应联合建模的条件模型，并针对两个反应的间歇性缺失考虑两个共享随机效应模型。采用基于马尔可夫链蒙特卡罗（MCMC）的贝叶斯方法进行参数估计和模型实现。通过一些模拟研究来研究所提出模型的验证和性能。所提出的模型还应用于分析一个减肥手术的真实数据集。
