Description
Dependent Censoring Regression Models with Cure Fraction.
Description
Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) <doi:10.1007/s10651-022-00549-0>.
README.md
CureDepCens
Cure dependent censoring regression models for long-term survival multivariate data.
Installation
You can install the development version of CureDepCens from GitHub with:
install.packages("devtools")
devtools::install_github("GabrielGrandemagne/CureDepCens")
Example
This is a basic example which shows you how to solve a common problem:
library(devtools)
#> Carregando pacotes exigidos: usethis
library(CureDepCens)
load_all()
#> ℹ Loading CureDepCens
Dogs_MimicData <- Dogs_MimicData
delta_t = ifelse(Dogs_MimicData$cens==1,1,0)
delta_c = ifelse(Dogs_MimicData$cens==2,1,0)
# MEP
fit <- cure_dep_censoring(formula = time ~ x1_cure + x2_cure | x_c1 + x_c2,
data = Dogs_MimicData,
delta_t = delta_t,
delta_c = delta_c,
ident = Dogs_MimicData$ident,
dist = "mep")
summary_cure(fit)
#>
#> MEP approach
#>
#> Name Estimate Std. Error CI INF CI SUP p-value
#> Alpha 2.034930 0.2005083 1.641933 2.427926 3.044e-26
#> Theta 0.7787554 0.4238412 0.000000 1.609484
#>
#> Coefficients Cure:
#>
#> Name Estimate Std. Error CI INF CI SUP p-value
#> Interc -0.6976047 0.1781988 -1.046874 -0.3483351 7.141e-33
#> x1_cur 0.514533 0.1703999 0.1805492 0.8485168 7.419e-18
#> x2_cur 0.2017428 0.08103922 0.04290593 0.3605797 0.001578
#>
#> Coefficients C:
#>
#> Name Estimate Std. Error CI INF CI SUP p-value
#> x_c1 0.03219111 0.1625781 -0.286462 0.3508442 0.1122
#> x_c2 -0.318467 0.1609394 -0.6339082 -0.003025754 4.682e-12
#>
#> ----------------------------------------------------------------------------------
#>
#> Information criteria:
#>
#> AIC BIC HQ
#> 510.9032 574.7666 536.194
Dogs_MimicData is our simulated data frame. For more information check the documentation for stored datasets.
head(Dogs_MimicData)
#> u v t c time event int x1_cure
#> 1 0.56788087 0.83359383 0.4131564 0.3614745 0.3614745 0 1 0
#> 2 0.66013804 0.72909631 1.0968927 2.1033648 1.0968927 1 1 1
#> 3 0.06854872 0.63332194 Inf 1.6510975 1.6510975 0 1 1
#> 4 0.88345952 0.57152197 0.6522436 8.6456149 0.6522436 1 1 1
#> 5 0.45431855 0.92452776 0.9258282 0.5216269 0.5216269 0 1 1
#> 6 0.12120571 0.02350277 Inf 10.9070711 5.1121398 0 1 1
#> x2_cure x_c1 x_c2 cens ident
#> 1 0.5228382 1.0403070 0 2 1
#> 2 -0.4207129 0.1071675 1 1 2
#> 3 -1.1207319 -1.4042911 0 2 3
#> 4 1.1764416 -0.7740067 1 1 4
#> 5 0.3891404 0.4973770 1 2 5
#> 6 0.5580893 -0.2278904 1 3 6
You can also plot the survival function
plot_cure(fit, scenario = "t")
