4.6 Article

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

Journal

STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 31, Issue 11, Pages 2037-2053

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802221108579

Keywords

Bounded cumulative hazard; Cox proportional hazard; high-dimensional covariates; mixture cure model; penalized generalized estimating equation

Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2016-06296, RGPIN-2017-04363]
  2. Canadian Institutes for Health Research (CIHR) [FDN-143297]
  3. Albert Boehringer I Chair in Pharmacoepidemiology
  4. National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, USA [UL1TR002489]

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In this article, several estimators and a variable selection procedure are proposed for assessing the effects of covariates on the cure rate and the risk of having the event of interest in survival data analysis.
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.

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