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A multiple imputation approach for semiparametric cure model with interval censored data

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  • Zhou, Jie
  • Zhang, Jiajia
  • McLain, Alexander C.
  • Cai, Bo

Abstract

The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval censored, the estimation of this model is challenging due to its complex data structure. A multiple imputation algorithm is proposed to obtain parameter and variance estimates for both the cure probability and the survival distribution of the uncured patients. The proposed approach can be easily implemented in commonly used statistical softwares, such as R and SAS, and its performance is comparable to fully parametric methods via comprehensive simulation studies. For illustration, the approach is applied to the 2000–2010 Greater Georgia breast cancer data set from the Surveillance, Epidemiology, and End Results Program.

Suggested Citation

  • Zhou, Jie & Zhang, Jiajia & McLain, Alexander C. & Cai, Bo, 2016. "A multiple imputation approach for semiparametric cure model with interval censored data," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 105-114.
    • Handle: RePEc:eee:csdana:v:99:y:2016:i:c:p:105-114
    DOI: 10.1016/j.csda.2016.01.013
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    References listed on IDEAS

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    1. Liu, Hao & Shen, Yu, 2009. "A Semiparametric Regression Cure Model for Interval-Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1168-1178.
    2. Peng, Yingwei, 2003. "Fitting semiparametric cure models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 481-490, January.
    3. Wei Pan, 2000. "A Multiple Imputation Approach to Cox Regression with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 199-203, March.
    4. Joseph G. Ibrahim & Ming-Hui Chen & Debajyoti Sinha, 2001. "Bayesian Semiparametric Models for Survival Data with a Cure Fraction," Biometrics, The International Biometric Society, vol. 57(2), pages 383-388, June.
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    Cited by:

    1. Philippe Lambert & Vincent Bremhorst, 2020. "Inclusion of time‐varying covariates in cure survival models with an application in fertility studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 333-354, January.

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