Random Generation of Survival Data
Implemented link functions for the mixture cure rate model
Inverse of the probability generating function
Linear predictors
Implemented link functions for the promotion time cure rate model with...
Generic quantile function
Random generation from accelerated failure time models
Random generation from accelerated hazard models
Random generation from extended hazard models
Frailties random generation
Random generation of type I and type II interval censored survival dat...
Random generation from proportional hazards models
Random generation from proportional odds models
The 'rsurv' package
Random generation from Yang and Prentice models
Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) <doi:10.48550/arXiv.2406.01750>.
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