Mixture cure rate models with neural network estimated nonparametric components

• Published in: Computational statistics Vol. 36; no. 4; pp. 2467 - 2489
• Main Authors: Xie, Yujing, Yu, Zhangsheng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Economic Theory/Quantitative Economics/Mathematical Methods; Mathematics and Statistics; Neural networks; Nonparametric statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Statistics; Deep learning; Survival analysis; EM algorithm; Consistency
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-021-01086-3

Survival data including potentially cured subjects are common in clinical studies and mixture cure rate models are often used for analysis. The non-cured probabilities are often predicted by non-parametric, high-dimensional, or even unstructured (e.g. image) predictors, which is a challenging task f...