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Log‐Logistic Regression Models for Survival Data

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  • Steve Bennett

Abstract

The log‐logistic distribution has a non‐monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log‐logistic regression model is described in which the hazard functions for separate samples converge with time. This also provides a linear model for the log odds on survival by any chosen time. The model is fitted on GLIM and an example is given of its use with lung cancer survival data.

Suggested Citation

  • Steve Bennett, 1983. "Log‐Logistic Regression Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 165-171, June.
    • Handle: RePEc:bla:jorssc:v:32:y:1983:i:2:p:165-171
    DOI: 10.2307/2347295
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    Cited by:

    1. Sanku Dey & Indranil Ghosh & Devendra Kumar, 2019. "Alpha-Power Transformed Lindley Distribution: Properties and Associated Inference with Application to Earthquake Data," Annals of Data Science, Springer, vol. 6(4), pages 623-650, December.
    2. Singh Housila P. & Mehta Vishal, 2017. "Improved Estimation of the Scale Parameter for Log-Logistic Distribution Using Balanced Ranked Set Sampling," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 53-74, March.
    3. Lucia Zanotto & Vladimir Canudas-Romo & Stefano Mazzuco, 2021. "A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 1-27, March.
    4. Ugofilippo Basellini & Vladimir Canudas-Romo & Adam Lenart, 2019. "Location–Scale Models in Demography: A Useful Re-parameterization of Mortality Models," European Journal of Population, Springer;European Association for Population Studies, vol. 35(4), pages 645-673, October.
    5. Xiaofang He & Wangxue Chen & Wenshu Qian, 2020. "Maximum likelihood estimators of the parameters of the log-logistic distribution," Statistical Papers, Springer, vol. 61(5), pages 1875-1892, October.
    6. K. F. Lam & Y. W. Lee & T. L. Leung, 2002. "Modeling Multivariate Survival Data by a Semiparametric Random Effects Proportional Odds Model," Biometrics, The International Biometric Society, vol. 58(2), pages 316-323, June.
    7. Ranjita Pandey & Pulkit Srivastava & Neera Kumari, 2021. "On some inferential aspects of length biased log-logistic model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 154-163, February.
    8. Trond Petersen, 1986. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates," Sociological Methods & Research, , vol. 14(3), pages 219-246, February.
    9. Chrys Caroni, 2022. "Regression Models for Lifetime Data: An Overview," Stats, MDPI, vol. 5(4), pages 1-11, December.
    10. Bahoo-Torodi, Aliasghar & Torrisi, Salvatore, 2022. "When do spinouts benefit from market overlap with parent firms?," Journal of Business Venturing, Elsevier, vol. 37(6).
    11. Hassan M. Okasha & Abdulkareem M. Basheer & Yuhlong Lio, 2022. "The E-Bayesian Methods for the Inverse Weibull Distribution Rate Parameter Based on Two Types of Error Loss Functions," Mathematics, MDPI, vol. 10(24), pages 1-27, December.
    12. Xifen Huang & Chaosong Xiong & Tao Jiang & Junfeng Lu & Jinfeng Xu, 2022. "Efficient Estimation and Inference in the Proportional Odds Model for Survival Data," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    13. Devendra Kumar & Anju Goyal, 2019. "Generalized Lindley Distribution Based on Order Statistics and Associated Inference with Application," Annals of Data Science, Springer, vol. 6(4), pages 707-736, December.
    14. Ateq Alghamedi & Sanku Dey & Devendra Kumar & Saeed A. Dobbah, 2020. "A New Extension of Extended Exponential Distribution with Applications," Annals of Data Science, Springer, vol. 7(1), pages 139-162, March.

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