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New statistical software for the proportional hazards model with current status data

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  • Mongoué-Tchokoté, Solange
  • Kim, Jong-Sung

Abstract

Currently, there are some statistical software packages such as intcox, survBayes, and BITE that are designed to analyze current status data (in which failure times are known to be either left- or right-censored) based on the proportional hazards model. They, however, either fail to directly provide standard errors for the estimated regression parameters or require frailty terms. As a result, practitioners often analyze their data using packages for right-censored data. By so doing, they mistreat left-censored observations as exact. This paper describes our newly developed statistical software for the proportional hazards model with current status data. The software is implemented in the R and C languages and consists of the following two simple steps: (a) find MLEs of the regression parameter and the cumulative hazard function; (b) compute the variance-covariance matrix of the regression parameter estimator by using the generalized missing information principle (GMIP) developed by Kim [Kim, J.S., 2003b. Efficient estimation for the proportional hazards model with left-truncated and Case 1 interval-censored data. Statista Sinica 13 (2), 519-537]. Our simulation study results show that our method works well in terms of bias, standard error, and power. By treating current status data as right-censored data, we also show the discrepancy in terms of bias, standard error, and power. Real examples are provided to illustrate the use of the software. This method can be extended to both general interval-censored data and truncated and interval-censored data.

Suggested Citation

  • Mongoué-Tchokoté, Solange & Kim, Jong-Sung, 2008. "New statistical software for the proportional hazards model with current status data," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4272-4286, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4272-4286
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    References listed on IDEAS

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    1. Jong S. Kim, 2003. "Maximum likelihood estimation for the proportional hazards model with partly interval‐censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 489-502, May.
    2. Pan, Wei & Chappell, Rick, 1998. "Computation of the NPMLE of distribution functions for interval censored and truncated data with applications to the Cox model," Computational Statistics & Data Analysis, Elsevier, vol. 28(1), pages 33-50, July.
    3. Tianxi Cai & Rebecca A. Betensky, 2003. "Hazard Regression for Interval-Censored Data with Penalized Spline," Biometrics, The International Biometric Society, vol. 59(3), pages 570-579, September.
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    Cited by:

    1. Cai, Bo & Lin, Xiaoyan & Wang, Lianming, 2011. "Bayesian proportional hazards model for current status data with monotone splines," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2644-2651, September.
    2. Wen, Chi-Chung & Chen, Yi-Hau, 2011. "Nonparametric maximum likelihood analysis of clustered current status data with the gamma-frailty Cox model," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1053-1060, February.
    3. Stephanie Chan & Xuan Wang & Ina Jazić & Sarah Peskoe & Yingye Zheng & Tianxi Cai, 2021. "Developing and evaluating risk prediction models with panel current status data," Biometrics, The International Biometric Society, vol. 77(2), pages 599-609, June.

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