Bernhardt Paul W, Zhang Daowen, Wang Huixia Judy
Department of Mathematics and Statistics, Villanova University, Villanova, PA, USA.
Department of Statistics, North Carolina State University, Raleigh, NC, USA.
Comput Stat Data Anal. 2015 May 1;85:37-53. doi: 10.1016/j.csda.2014.11.011.
Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set.
联合建模技术已成为研究响应变量与一个或多个纵向协变量之间关联的常用策略。受GenIMS研究的启发，在该研究中使用经过删失的纵向生物标志物对生存事件进行建模很有意义，因此提出了一种联合模型，用于描述二元结局与受检测限影响的多个纵向协变量之间的关系。开发了一种快速近似期望最大化（EM）算法，该算法将算法E步中的积分维度降至一维，而与联合模型中随机效应的数量无关。数值研究表明，所提出的近似EM算法在纵向协变量存在删失和不存在删失的情况下，都能得到令人满意的参数估计和方差估计。将近似EM算法应用于分析GenIMS数据集。
