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frailtypack: An R Package for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation

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  • Rondeau, Virginie
  • Marzroui, Yassin
  • Gonzalez, Juan R.

Abstract

Frailty models are very useful for analysing correlated survival data, when observations are clustered into groups or for recurrent events. The aim of this article is to present the new version of an R package called frailtypack. This package allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical research. It is well adapted to the analysis of recurrent events such as cancer relapses and/or terminal events (death or lost to follow-up). The approach uses maximum penalized likelihood estimation. Right-censored or left-truncated data are considered. It also allows stratification and time-dependent covariates during analysis.

Suggested Citation

  • Rondeau, Virginie & Marzroui, Yassin & Gonzalez, Juan R., 2012. "frailtypack: An R Package for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i04).
    • Handle: RePEc:jss:jstsof:v:047:i04
    DOI: http://hdl.handle.net/10.18637/jss.v047.i04
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    1. Gerda Claeskens & Rosemary Nguti & Paul Janssen, 2008. "One-sided tests in shared frailty models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 69-82, May.
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    1. Guerzoni, Marco & Jordan, Alexander, 2016. "“Cursed is the ground because of you”: Religion, Ethnicity, and the Adoption of Fertilizers in Rural Ethiopia," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201605, University of Turin.
    2. Sangita Kulathinal & Isha Dewan, 2023. "Weighted U-statistics for likelihood-ratio ordering of bivariate data," Statistical Papers, Springer, vol. 64(2), pages 705-735, April.
    3. Yassin Mazroui & Audrey Mauguen & Simone Mathoulin-Pélissier & Gaetan MacGrogan & Véronique Brouste & Virginie Rondeau, 2016. "Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 191-215, April.
    4. Philip Hougaard, 2022. "Choice of time scale for analysis of recurrent events data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 700-722, October.
    5. Bedair, Khaled & Hong, Yili & Li, Jie & Al-Khalidi, Hussein R., 2016. "Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 161-173.
    6. Per Kragh Andersen & Jules Angst & Henrik Ravn, 2019. "Modeling marginal features in studies of recurrent events in the presence of a terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 681-695, October.
    7. Lawrence Kryzanowski & Yanting Wu, 2023. "Signaling effects of recurrent list‐price reductions on the likelihood of house sales," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 99-130, February.
    8. Peter C. Austin, 2017. "A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications," International Statistical Review, International Statistical Institute, vol. 85(2), pages 185-203, August.
    9. Liesbeth E C Wijnvoord & Sandra Brouwer & Jan Buitenhuis & Jac J L van der Klink & Michiel R de Boer, 2016. "Indications of a Scarring Effect of Sickness Absence Periods in a Cohort of Higher Educated Self-Employed," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-9, May.
    10. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
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    12. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    13. Di Shu & Jessica G. Young & Sengwee Toh & Rui Wang, 2021. "Variance estimation in inverse probability weighted Cox models," Biometrics, The International Biometric Society, vol. 77(3), pages 1101-1117, September.
    14. Munda, Marco & Rotolo, Federico & Legrand, Catherine, 2012. "parfm: Parametric Frailty Models in R," LIDAM Discussion Papers ISBA 2012005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Chien-Lin Su & Russell J. Steele & Ian Shrier, 2021. "The semiparametric accelerated trend-renewal process for recurrent event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 357-387, July.
    16. Travis L Dynes & Jennifer A Berry & Keith S Delaplane & Berry J Brosi & Jacobus C de Roode, 2019. "Reduced density and visually complex apiaries reduce parasite load and promote honey production and overwintering survival in honey bees," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-16, May.
    17. Alexander Jordan & Marco Guerzoni, 2021. "“Cursed is the ground because of you”:," Journal of Evolutionary Economics, Springer, vol. 31(3), pages 853-890, July.
    18. Ajai S Gaur & Chinmay Pattnaik & Deeksha Singh & Jeoung Yul Lee, 2019. "Internalization advantage and subsidiary performance: The role of business group affiliation and host country characteristics," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 50(8), pages 1253-1282, October.
    19. Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.
    20. Zhongwen Zhang & Xiaoguang Wang & Yingwei Peng, 2022. "An additive hazards frailty model with semi-varying coefficients," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 116-138, January.
    21. Andreas Groll & Gerhard Tutz, 2017. "Variable selection in discrete survival models including heterogeneity," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 305-338, April.
    22. Andreas Groll & Trevor Hastie & Gerhard Tutz, 2017. "Selection of effects in Cox frailty models by regularization methods," Biometrics, The International Biometric Society, vol. 73(3), pages 846-856, September.
    23. Prabhashi W. Withana Gamage & Christopher S. McMahan & Lianming Wang, 2023. "A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 188-212, January.

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