论文标题
带有半参数比例危害子模型的潜在类别分析用于事件时间数据
Latent Class Analysis with Semi-parametric Proportional Hazards Submodel for Time-to-event Data
论文作者
论文摘要
潜在类别分析（LCA）是研究疾病人群与事件时间数据的异质性的有用工具。我们提出了一种基于非参数最大似然估计量（NPMLE）的新方法，该方法促进了理论上验证的协变量效应和累积危害功能的推理程序。我们通过广泛的仿真研究评估了提出的方法，并证明了与标准COX回归模型相比的预测性能的提高。我们进一步说明了提出的方法通过应用于轻度认知障碍（MCI）同类数据集的实际实用性。
Latent class analysis (LCA) is a useful tool to investigate the heterogeneity of a disease population with time-to-event data. We propose a new method based on non-parametric maximum likelihood estimator (NPMLE), which facilitates theoretically validated inference procedure for covariate effects and cumulative hazard functions. We assess the proposed method via extensive simulation studies and demonstrate improved predictive performance over standard Cox regression model. We further illustrate the practical utility of the proposed method through an application to a mild cognitive impairment (MCI) cohort dataset.
