A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data
针对多元纵向数据与生存时间联合模型中随机效应维度高导致计算慢的问题，提出一种近似EM算法来缓解维度灾难，并通过模拟和两个临床试验数据验证其可扩展性和准确性。
Joint models are an increasingly popular way to characterise the relationship between one or more longitudinal responses and an event of interest. However, for multivariate joint models the increased dimensionality and complexity of random effects present in the model specification are commensurate with increased computing time, hampering the implementation of many classic approaches. An approximate EM algorithm which ameliorates the so-called ‘curse of dimensionality’ is developed. The scaleability and accuracy of the proposed method are demonstrated via two simulation studies and applied to data arising from two clinical trials in the disease areas of cirrhosis and Alzheimer's disease, each with three biomarkers.