Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies show that R-INLA reduces the computation time substantially as well as the variability of the parameter estimates and improves the model convergence compared to frailtypack. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where R-INLA suggests a stronger association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study while frailtypack had convergence issues. Our study suggests that the Bayesian approach using R-INLA algorithm enables broader applications of the two-part joint model to clinical applications.

翻译：长期半连续生物标志和终点事件的双部分联合模型最近根据经常估计采用。生物标志分布被分解成正值和正值预期值的概率。 共享随机效应可以代表生物标志和终点事件之间的关联结构。 与生物标志单一回归模型相比,计算负担增加。 在这方面,R包的脆弱包装中实施的频繁估计对复杂模型(即,大量参数和随机效应的广度临床应用)可能具有挑战性。 作为替代办法,我们提议对基于综合内斯特·拉韦普综合模拟(INLA)算法的两部分联合模型进行巴伊西亚估计,以缓解计算负担和适应更复杂的模型。我们的模拟研究表明,R-INLA与标准模型估计的变异性以及参数估计的变异性,并改进了模型与疲软的组合方法。我们对比了在两次随机癌症临床实验分析中采用巴伊和频繁的方法,其中显示,在BIRCOR和IMIMA研究中采用更强有力的结果, 显示,在BILA和BIA的对比性研究中采用更强有力的结果。