College of Mathematics and Statistics, Shenzhen University, Shenzhen, China.
School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China.
Stat Med. 2021 Dec 20;40(29):6590-6604. doi: 10.1002/sim.9200. Epub 2021 Sep 15.
A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.
提出了一种具有潜在变量的混合比例风险治愈模型。该模型通过比例风险模型和逻辑斯蒂模型分别评估观察到的和潜在的风险因素对未治愈个体的风险和治愈率的影响。通过相关多项指标进行因子分析来测量潜在变量。通过高斯二次技术进行最大似然估计，该技术通过对潜在变量的积分进行逼近。对于未治愈个体的未指定基线风险，使用分段常数函数。可以使用 SAS Proc NLMIXED 方便地实现所提出的方法。通过模拟研究来评估所提出方法的性能。提供了一个关于 2 型糖尿病患者慢性肾脏病危险因素的研究的应用案例。
