近年来，纵向和生存数据联合模型（JMLS）被广泛用于研究临床试验中纵向数据和生存数据之间的关系。但是，现有的研究主要集中在独立生存数据。在许多临床试验中，生存数据可能是双变量相关的。为此，本文提出了一种新的 JMLS，可容纳多变量纵向和双变量相关的时间事件数据。非参数边际生存风险函数转换为二元正态随机变量。采用贝叶斯惩罚样条来近似未知的基线危险函数。将 Metropolis-Hastings 算法合并到 Gibbs 采样器中，我们开发了一种贝叶斯自适应 Lasso 方法来同时估计参数和基线危险函数，并在所考虑的 JMLS 中选择重要的预测变量。使用模拟研究和国际乳腺癌研究组的示例来说明所提出的方法。© 2023 John Wiley

Joint models for longitudinal and survival data (JMLSs) are widely used to investigate the relationship between longitudinal and survival data in clinical trials in recent years. But, the existing studies mainly focus on independent survival data. In many clinical trials, survival data may be bivariately correlated. To this end, this paper proposes a novel JMLS accommodating multivariate longitudinal and bivariate correlated time-to-event data. Nonparametric marginal survival hazard functions are transformed to bivariate normal random variables. Bayesian penalized splines are employed to approximate unknown baseline hazard functions. Incorporating the Metropolis-Hastings algorithm into the Gibbs sampler, we develop a Bayesian adaptive Lasso method to simultaneously estimate parameters and baseline hazard functions, and select important predictors in the considered JMLS. Simulation studies and an example taken from the International Breast Cancer Study Group are used to illustrate the proposed methodologies.© 2023 John Wiley & Sons Ltd.