Literature DB >> 29593450

flexsurv: A Platform for Parametric Survival Modeling in R.

Christopher H Jackson1.   

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.

Entities:  

Keywords:  multi-state models; multistate models; survival

Year:  2016        PMID: 29593450      PMCID: PMC5868723          DOI: 10.18637/jss.v070.i08

Source DB:  PubMed          Journal:  J Stat Softw        ISSN: 1548-7660            Impact factor:   6.440


  19 in total

1.  Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.

Authors:  Patrick Royston; Mahesh K B Parmar
Journal:  Stat Med       Date:  2002-08-15       Impact factor: 2.373

2.  Selection of important variables and determination of functional form for continuous predictors in multivariable model building.

Authors:  Willi Sauerbrei; Patrick Royston; Harald Binder
Journal:  Stat Med       Date:  2007-12-30       Impact factor: 2.373

3.  Flexible parametric models for relative survival, with application in coronary heart disease.

Authors:  Christopher P Nelson; Paul C Lambert; Iain B Squire; David R Jones
Journal:  Stat Med       Date:  2007-12-30       Impact factor: 2.373

4.  Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.

Authors:  Marta Fiocco; Hein Putter; Hans C van Houwelingen
Journal:  Stat Med       Date:  2008-09-20       Impact factor: 2.373

Review 5.  Frailty models for survival data.

Authors:  P Hougaard
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

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Authors:  H G Müller; J L Wang
Journal:  Biometrics       Date:  1994-03       Impact factor: 2.571

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Authors:  D Heng; L D Sharples; K McNeil; S Stewart; T Wreghitt; J Wallwork
Journal:  J Heart Lung Transplant       Date:  1998-12       Impact factor: 10.247

8.  A general framework for parametric survival analysis.

Authors:  Michael J Crowther; Paul C Lambert
Journal:  Stat Med       Date:  2014-09-15       Impact factor: 2.373

9.  Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

Authors:  Michael J Crowther; Maxime P Look; Richard D Riley
Journal:  Stat Med       Date:  2014-05-01       Impact factor: 2.373

10.  Survival extrapolation in the presence of cause specific hazards.

Authors:  Tatiana Benaglia; Christopher H Jackson; Linda D Sharples
Journal:  Stat Med       Date:  2014-11-20       Impact factor: 2.373

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