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Mixture cure rate models with neural network estimated nonparametric components

Author

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  • Yujing Xie

    (Shanghai Jiao Tong University)

  • Zhangsheng Yu

    (Shanghai Jiao Tong University)

Abstract

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 for traditional nonparametric methods such as spline and local kernel. We propose to use the neural network to model the nonparametric or unstructured predictors’ effect in cure rate models and retain the proportional hazards structure due to its explanatory ability. We estimate the parameters by Expectation–Maximization algorithm. Estimators are showed to be consistent. Simulation studies show good performance in both prediction and estimation. Finally, we analyze Open Access Series of Imaging Studies data to illustrate the practical use of our methods.

Suggested Citation

  • Yujing Xie & Zhangsheng Yu, 2021. "Mixture cure rate models with neural network estimated nonparametric components," Computational Statistics, Springer, vol. 36(4), pages 2467-2489, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01086-3
    DOI: 10.1007/s00180-021-01086-3
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    References listed on IDEAS

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    1. Othus, Megan & Li, Yi & Tiwari, Ram C., 2009. "A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1241-1250.
    2. Lu Wang & Pang Du & Hua Liang, 2012. "Two-Component Mixture Cure Rate Model with Spline Estimated Nonparametric Components," Biometrics, The International Biometric Society, vol. 68(3), pages 726-735, September.
    3. Hong‐Bin Fang & Gang Li & Jianguo Sun, 2005. "Maximum Likelihood Estimation in a Semiparametric Logistic/Proportional‐Hazards Mixture Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(1), pages 59-75, March.
    4. Tianlei Chen & Pang Du, 2018. "Mixture cure rate models with accelerated failures and nonparametric form of covariate effects," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 216-237, January.
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    Cited by:

    1. Austin Menger & Md. Tuhin Sheikh & Ming-Hui Chen, 2024. "Bayesian Modeling of Survival Data in the Presence of Competing Risks with Cure Fractions and Masked Causes," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 199-227, November.
    2. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

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