Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach

被引:5
作者
Su, Chien-Lin [1 ,2 ,3 ]
Chiou, Sy Han [4 ]
Lin, Feng-Chang [5 ]
Platt, Robert W. [1 ,2 ]
机构
[1] McGill Univ, Dept Epidemiol Biostat & Occupat Hlth, Montreal, PQ, Canada
[2] Jewish Gen Hosp, Lady Davis Inst, Ctr Clin Epidemiol, Montreal, PQ, Canada
[3] Peri & Post Approval Studies Strateg & Sci Affair, Montreal, PQ, Canada
[4] Univ Texas Dallas, Dept Math Sci, Richardson, TX 75083 USA
[5] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27515 USA
基金
美国国家卫生研究院; 加拿大自然科学与工程研究理事会;
关键词
Bounded cumulative hazard; Cox proportional hazard; high-dimensional covariates; mixture cure model; penalized generalized estimating equation; GENERALIZED ESTIMATING EQUATIONS; LONG-TERM; PENALIZED LIKELIHOOD; PROPORTIONAL HAZARDS; MIXTURE MODEL; LINEAR-MODELS; CENSORED-DATA; R-PACKAGE; CANCER;
D O I
10.1177/09622802221108579
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:2037 / 2053
页数:17
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