Department of Epidemiology, Biostatistics and Occupational Health, 5620McGill University, Montréal, Québec, Canada.
Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada.
Stat Methods Med Res. 2022 Nov;31(11):2037-2053. doi: 10.1177/09622802221108579. Epub 2022 Jun 27.
In biomedical studies, survival data with a cure fraction (the proportion of subjects cured of disease) are commonly encountered. The mixture cure and bounded cumulative hazard models are two main types of cure fraction models when analyzing survival data with long-term survivors. In this article, in the framework of the Cox proportional hazards mixture cure model and bounded cumulative hazard model, we propose several estimators utilizing pseudo-observations to assess the effects of covariates on the cure rate and the risk of having the event of interest for survival data with a cure fraction. A variable selection procedure is also presented based on the pseudo-observations using penalized generalized estimating equations for proportional hazards mixture cure and bounded cumulative hazard models. Extensive simulation studies are conducted to examine the proposed methods. The proposed technique is demonstrated through applications to a melanoma study and a dental data set with high-dimensional covariates.
在生物医学研究中，常遇到带有治愈部分（疾病治愈的受试者比例）的生存数据。当分析具有长期幸存者的生存数据时，混合治愈和有界累积风险模型是治愈部分模型的两种主要类型。在本文中，在 Cox 比例风险混合治愈模型和有界累积风险模型的框架下，我们提出了几种利用伪观测值的估计器，以评估协变量对治愈率和有兴趣事件的风险的影响，这些估计器适用于带有治愈部分的生存数据。还提出了一种基于惩罚广义估计方程的变量选择程序，用于比例风险混合治愈和有界累积风险模型。通过广泛的模拟研究来检验所提出的方法。通过对黑色素瘤研究和具有高维协变量的牙科数据集的应用，展示了所提出的技术。
