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flexsurv: A Platform for Parametric Survival Modeling in R

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  • Jackson, Christopher

Abstract

flexsurv is an R package for fully-parametric modeling of survival data. Any parametric time-to-event distribution may be fitted if the user supplies a probability density or hazard function, and ideally also their cumulative versions. Standard survival distributions are built in, including the three and four-parameter generalized gamma and F distributions. Any parameter of any distribution can be modeled as a linear or log-linear function of covariates. The package also includes the spline model of Royston and Parmar (2002), in which both baseline survival and covariate effects can be arbitrarily flexible parametric functions of time. The main model-fitting function, flexsurvreg, uses the familiar syntax of survreg from the standard survival package (Therneau 2016). Censoring or left-truncation are specified in 'Surv' objects. The models are fitted by maximizing the full log-likelihood, and estimates and confidence intervals for any function of the model parameters can be printed or plotted. flexsurv also provides functions for fitting and predicting from fully-parametric multi-state models, and connects with the mstate package (de Wreede, Fiocco, and Putter 2011). This article explains the methods and design principles of the package, giving several worked examples of its use.

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  • Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
    • Handle: RePEc:jss:jstsof:v:070:i08
    DOI: http://hdl.handle.net/10.18637/jss.v070.i08
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    References listed on IDEAS

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    16. Gabrielle Jongeneel & Marjolein J. E. Greuter & Felice N. Erning & Miriam Koopman & Jan P. Medema & Raju Kandimalla & Ajay Goel & Luis Bujanda & Gerrit A. Meijer & Remond J. A. Fijneman & Martijn G. H, 2020. "Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(7), pages 1059-1073, September.
    17. I. Oostrum & T. A. Russell-Smith & M. Jakobsson & J. Torup Østby & B. Heeg, 2022. "Cost-Effectiveness of Inotuzumab Ozogamicin Compared to Standard of Care Chemotherapy for Treating Relapsed or Refractory Acute Lymphoblastic Leukaemia Patients in Norway and Sweden," PharmacoEconomics - Open, Springer, vol. 6(1), pages 47-62, January.
    18. Suvra Pal & Hongbo Yu & Zachary D. Loucks & Ian M. Harris, 2020. "Illustration of the Flexibility of Generalized Gamma Distribution in Modeling Right Censored Survival Data: Analysis of Two Cancer Datasets," Annals of Data Science, Springer, vol. 7(1), pages 77-90, March.
    19. Lauren Scott & Chris Rogers, 2016. "Creating summary tables using the sumtable command," United Kingdom Stata Users' Group Meetings 2016 05, Stata Users Group.
    20. Machado, Robson J.M. & van den Hout, Ardo & Marra, Giampiero, 2021. "Penalised maximum likelihood estimation in multi-state models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
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    22. Camarda, Carlo Giovanni, 2022. "The curse of the plateau. Measuring confidence in human mortality estimates at extreme ages," Theoretical Population Biology, Elsevier, vol. 144(C), pages 24-36.
    23. Jodi Gray & Thomas Sullivan & Nicholas R. Latimer & Amy Salter & Michael J. Sorich & Robyn L. Ward & Jonathan Karnon, 2021. "Extrapolation of Survival Curves Using Standard Parametric Models and Flexible Parametric Spline Models: Comparisons in Large Registry Cohorts with Advanced Cancer," Medical Decision Making, , vol. 41(2), pages 179-193, February.
    24. Linh Hoang Khanh Dang & Carlo Giovanni Camarda & France Meslé & Nadine Ouellette & Jean-Marie Robine & Jacques Vallin, 2023. "The question of the human mortality plateau: Contrasting insights by longevity pioneers," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(11), pages 321-338.
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