IDEAS home Printed from https://ideas.repec.org/a/spr/jstada/v3y2016i1d10.1186_s40488-016-0054-z.html
   My bibliography  Save this article

Generalized log-logistic proportional hazard model with applications in survival analysis

Author

Listed:
  • Shahedul A. Khan

    (University of Saskatchewan)

  • Saima K. Khosa

    (University of Saskatchewan)

Abstract

Proportional hazard (PH) models can be formulated with or without assuming a probability distribution for survival times. The former assumption leads to parametric models, whereas the latter leads to the semi-parametric Cox model which is by far the most popular in survival analysis. However, a parametric model may lead to more efficient estimates than the Cox model under certain conditions. Only a few parametric models are closed under the PH assumption, the most common of which is the Weibull that accommodates only monotone hazard functions. We propose a generalization of the log-logistic distribution that belongs to the PH family. It has properties similar to those of log-logistic, and approaches the Weibull in the limit. These features enable it to handle both monotone and nonmonotone hazard functions. Application to four data sets and a simulation study revealed that the model could potentially be very useful in adequately describing different types of time-to-event data.

Suggested Citation

  • Shahedul A. Khan & Saima K. Khosa, 2016. "Generalized log-logistic proportional hazard model with applications in survival analysis," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-18, December.
    • Handle: RePEc:spr:jstada:v:3:y:2016:i:1:d:10.1186_s40488-016-0054-z
    DOI: 10.1186/s40488-016-0054-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40488-016-0054-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40488-016-0054-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fushing Hsieh & Yi-Kuan Tseng & Jane-Ling Wang, 2006. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited," Biometrics, The International Biometric Society, vol. 62(4), pages 1037-1043, December.
    2. Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
    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. Vincent Abiodun Michael & Ismaila Adewale Bolarinwa, 2020. "Parametric Survival Modeling of Tuberculosis Data- A Case Study of Federal Medical Centre, Bida, Nigeria," Modern Applied Science, Canadian Center of Science and Education, vol. 14(7), pages 1-37, July.
    2. Abdisalam Hassan Muse & Samuel Mwalili & Oscar Ngesa & Christophe Chesneau & Afrah Al-Bossly & Mahmoud El-Morshedy, 2022. "Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data," Mathematics, MDPI, vol. 10(20), pages 1-41, October.
    3. Worku B. Ewnetu & Irène Gijbels & Anneleen Verhasselt, 2023. "Flexible two-piece distributions for right censored survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 34-65, January.
    4. Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.

    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. Yih‐Huei Huang & Wen‐Han Hwang & Fei‐Yin Chen, 2016. "Improving efficiency using the Rao–Blackwell theorem in corrected and conditional score estimation methods for joint models," Biometrics, The International Biometric Society, vol. 72(4), pages 1136-1144, December.
    2. Lisa M. McCrink & Adele H. Marshall & Karen J. Cairns, 2013. "Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients," International Statistical Review, International Statistical Institute, vol. 81(2), pages 249-269, August.
    3. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    4. Xavier Piulachs & Ramon Alemany & Montserrat Guillen, 2014. "A joint longitudinal and survival model with health care usage for insured elderly," Working Papers 2014-07, Universitat de Barcelona, UB Riskcenter.
    5. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    6. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    7. Chen, Chyong-Mei & Shen, Pao-sheng & Tseng, Yi-Kuan, 2018. "Semiparametric transformation joint models for longitudinal covariates and interval-censored failure time," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 116-127.
    8. Shahedul A. Khan & Nyla Basharat, 2022. "Accelerated failure time models for recurrent event data analysis and joint modeling," Computational Statistics, Springer, vol. 37(4), pages 1569-1597, September.
    9. Walter Dempsey & Peter McCullagh, 2018. "Survival models and health sequences," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 550-584, October.
    10. Hongyuan Cao & Mathew M. Churpek & Donglin Zeng & Jason P. Fine, 2015. "Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1187-1196, September.
    11. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    12. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    13. Roula Tsonaka & Geert Verbeke & Emmanuel Lesaffre, 2009. "A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness," Biometrics, The International Biometric Society, vol. 65(1), pages 81-87, March.
    14. Oi, Katsuya, 2020. "Disuse as time away from a cognitively demanding job; how does it temporally or developmentally impact late-life cognition?," Intelligence, Elsevier, vol. 82(C).
    15. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    16. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    17. Marlena Maziarz & Patrick Heagerty & Tianxi Cai & Yingye Zheng, 2017. "On longitudinal prediction with time-to-event outcome: Comparison of modeling options," Biometrics, The International Biometric Society, vol. 73(1), pages 83-93, March.
    18. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.
    19. de Godoy, Isabelle Bueno Silva & McGrane-Corrigan, Blake & Mason, Oliver & Moral, Rafael de Andrade & Godoy, Wesley Augusto Conde, 2023. "Plant-host shift, spatial persistence, and the viability of an invasive insect population," Ecological Modelling, Elsevier, vol. 475(C).
    20. Haiqun Lin & Zhenchao Guo & Peter N. Peduzzi & Thomas M. Gill & Heather G. Allore, 2008. "A Semiparametric Transition Model with Latent Traits for Longitudinal Multistate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1032-1042, December.

    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:spr:jstada:v:3:y:2016:i:1:d:10.1186_s40488-016-0054-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.