Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, 650091, People's Republic of China.
Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
Lifetime Data Anal. 2023 Oct;29(4):888-918. doi: 10.1007/s10985-023-09608-5. Epub 2023 Aug 15.
We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis-Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.
我们考虑了一类新的半参数联合模型，用于具有相依删失的多元纵向和生存数据。在这些模型中，通过具有线性约束的新型惩罚样条（P-样条）拟合未知形式的累积基线风险函数。通过正态变换模型来适应感兴趣的失效时间和删失时间之间的相关性，其中非参数边缘生存函数和删失函数都转换为具有二元正态联合分布的标准正态随机变量。基于一种混合算法以及 Gibbs 抽样器中的 Metropolis-Hastings 算法，我们提出了一种可行的贝叶斯方法来同时估计感兴趣的未知参数，并拟合基线生存和删失函数。通过密集的模拟研究来评估所提出方法的性能。还通过分析来自国际乳腺癌研究小组的数据集来说明所提出方法的使用。
