IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i9p4272-4286.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00062-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
    2. Li‐Pang Chen & Bangxu Qiu, 2023. "Analysis of length‐biased and partly interval‐censored survival data with mismeasured covariates," Biometrics, The International Biometric Society, vol. 79(4), pages 3929-3940, December.
    3. Audrey Boruvka & Richard J. Cook, 2015. "A Cox-Aalen Model for Interval-censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 414-426, June.
    4. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
    5. Li, Jinqing & Ma, Jun, 2019. "Maximum penalized likelihood estimation of additive hazards models with partly interval censoring," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 170-180.
    6. Min Zhang & Marie Davidian, 2008. "“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data," Biometrics, The International Biometric Society, vol. 64(2), pages 567-576, June.
    7. Lianming Wang & David B. Dunson, 2011. "Semiparametric Bayes' Proportional Odds Models for Current Status Data with Underreporting," Biometrics, The International Biometric Society, vol. 67(3), pages 1111-1118, September.
    8. Zhiguo Li & Kouros Owzar, 2016. "Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 476-486, June.
    9. Gürler, Ülkü & Deniz Yenigün, C., 2011. "Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2856-2870, October.
    10. Ying Wu & Richard J. Cook, 2015. "Penalized regression for interval‐censored times of disease progression: Selection of HLA markers in psoriatic arthritis," Biometrics, The International Biometric Society, vol. 71(3), pages 782-791, September.
    11. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2017. "Semiparametric estimation of the accelerated failure time model with partly interval‐censored data," Biometrics, The International Biometric Society, vol. 73(4), pages 1161-1168, December.
    12. Li, Chenxi, 2016. "Cause-specific hazard regression for competing risks data under interval censoring and left truncation," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 197-208.
    13. Prabhashi W. Withana Gamage & Monica Chaudari & Christopher S. McMahan & Edwin H. Kim & Michael R. Kosorok, 2020. "An extended proportional hazards model for interval-censored data subject to instantaneous failures," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 158-182, January.
    14. Ma, Shuangge & Kosorok, Michael R., 2005. "Robust semiparametric M-estimation and the weighted bootstrap," Journal of Multivariate Analysis, Elsevier, vol. 96(1), pages 190-217, September.
    15. Dursun Aydin & Ersin Yilmaz, 2021. "Censored Nonparametric Time-Series Analysis with Autoregressive Error Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 169-202, August.
    16. Liu, Wenting & Li, Huiqiong & Tang, Niansheng & Lyu, Jun, 2024. "Variational Bayesian approach for analyzing interval-censored data under the proportional hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
    17. Wei Pan & Rick Chappell, 2002. "Estimation in the Cox Proportional Hazards Model with Left-Truncated and Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 64-70, March.
    18. Yuhyun Park & Lu Tian & L. J. Wei, 2004. "One- and Two-Sample Nonparametric Inference Procedures in the Presence of Dependent Censoring," Harvard University Biostatistics Working Paper Series 1012, Berkeley Electronic Press.
    19. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2018. "Semiparametric regression analysis of interval‐censored data with informative dropout," Biometrics, The International Biometric Society, vol. 74(4), pages 1213-1222, December.
    20. Tomoyuki Sugimoto, 2013. "Asymptotic distribution of the nonparametric distribution estimator based on a martingale approach in doubly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 859-888, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4272-4286. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.