# DepCens

The package DepCens uses dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions.

## Installation

The latest stable version can be installed from CRAN:

``install.packages('DepCens')``

The latest development version can be installed from GitHub with:

``````install.packages("devtools")
devtools::install_github('GabrielGrandemagne/DepCens')``````

## Example

This is a basic example which shows you how to solve a common problem:

``````library(DepCens)
#KidneyMimic is our simulated data frame
delta_t <- ifelse(KidneyMimic\$cens==1,1,0)
delta_c <- ifelse(KidneyMimic\$cens==2,1,0)
fit <- dependent.censoring(formula = time ~ x1 + x2 | x3 + x1, data=KidneyMimic, delta_t=delta_t,
delta_c=delta_c, ident=KidneyMimic\$ident, dist = "weibull")
summary_dc(fit)
#>
#> Weibull approach
#>
#> Name  Estimate    Std. Error  CI INF      CI SUP      p-value
#> Alpha    1.386908    0.3175058   0.7645967   2.009219    1.253e-05
#> Sigma    0.6473139   0.219075    0.2179268   1.076701
#>
#> Coefficients T:
#>
#> Name  Estimate    Std. Error  CI INF      CI SUP      p-value
#> x1   0.08110233  0.02140495  0.03914863  0.123056    0.0001513
#> x2   -1.399794   0.2460258   -1.882005   -0.9175837  1.273e-08
#>
#> Coefficients C:
#>
#> Name  Estimate    Std. Error  CI INF      CI SUP      p-value
#> x3   0.2307375   0.1853012   -0.1324529  0.5939279   0.2131
#> x1   0.1953483   0.03854422  0.1198016   0.2708949   4.017e-07
#>
#> ----------------------------------------------------------------------------------
#>
#> Information criteria:
#>
#> AIC   BIC      HQ
#> 404.3393 434.0241 416.3523``````

KidneyMimic is our simulated data frame. For more information check the documentation for stored datasets.

``````head(KidneyMimic)
#>   ident      time event         x1 x2          x3 cens delta_t delta_c
#> 1     1 1.7828475     0  4.6770531  1  2.43961938    2       0       1
#> 2     2 6.3723589     0  0.1628727  1 -1.24630803    3       0       0
#> 3     3 6.6803247     1 -2.1962148  1 -0.73713564    1       1       0
#> 4     4 0.6975475     0  2.7430873  0 -0.31424253    2       0       1
#> 5     5 5.1130483     0 -1.1663762  1  0.03064846    2       0       1
#> 6     6 2.8189839     0  3.7558997  1 -0.52617419    2       0       1``````

You can also plot the survival function

``plot_dc(fit, scenario = "t")``