flexsurv: A Platform for Parametric Survival Modeling in R.
By C. Jackson. Journal of Statistical Software, 2016
548 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0dc6303613d9fdb1ad44ccbcf1ecedd8d0cda34a
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- lifelines: survival analysis in Python (2019, methodology): “For example, a survival analysis model may be fit using R’s survival-package (Terry M Therneau, 2015) or flexsurv (Christopher Jackson, 2016), but needs to be deployed into a Python system.”