Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Math Biosci. 2021 May;335:108586. doi: 10.1016/j.mbs.2021.108586. Epub 2021 Mar 15.
Multistate statistical models are often used to characterize the complex multi-compartment progression of the disease such as cancer. However, the derivation of multistate transition kernels is often involved with the intractable convolution that requires intensive computation. Moreover, the estimation of parameters pertaining to transition kernel requires the individualized time-stamped history data while the traditional likelihood function forms are constructed. In this paper, we came up with a novel likelihood function derived from Laplace transformation-based transition probabilities in conjunction with Expectation-Maximization algorithm to estimate parameters. The proposed method was applied to two large population-based screening data with only aggregated count data without relying on individual time-stamped history data.
多状态统计模型常用于描述癌症等复杂的多部位疾病进展。然而，多状态转移核的推导通常涉及难以处理的卷积，需要大量计算。此外，转移核参数的估计需要与传统似然函数形式构建的个体化时间戳历史数据相关。在本文中，我们提出了一种新的似然函数，该函数源自基于拉普拉斯变换的转移概率，结合期望最大化算法来估计参数。该方法应用于两个大型基于人群的筛查数据，仅使用聚合计数数据，而不依赖于个体时间戳历史数据。
