knitr::opts_chunk$set( echo = TRUE, collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
This package aims to estimate Nonparametric Cumulative-Incidence Based Estimation of the Ratios of Sub-Hazard Ratios to Cause-Specific Hazard Ratios.
You can install the latest version of hrcomprisk
in CRAN or the development version from Github:
# Install hrcomprisk from CRAN install.packages("hrcomprisk") # Or the development version from GitHub: # install.packages("devtools") devtools::install_github("AntiportaD/hrcomprisk")
hrcomprsk
packageYou can use the dataset provided by the authors from the CKiD study, wich has the necessary variables to run the package.
library(hrcomprisk) data <- hrcomprisk::dat_ckid dim(data) #dimensions names(data) #variable names
The package will create a data.frame
object with the cumulative incidence of each competing risk for each exposure group. We can use the CRCumInc
fuction.
mydat.CIF<-CRCumInc(df=data, time=exit, event=event, exposed=b1nb0, print.attr=T)
We can also obtain two different plots using the plotCIF
function:
plots<-plotCIF(cifobj=mydat.CIF, maxtime = 20, eoi = 1)
In order to get confidence intervals to the ratio of Hazard Ratios (Rk), we can use the bootCRCumInc
function:
ciCIF<-bootCRCumInc(df=data, exit=exit, event=event, exposure=b1nb0, rep=100, print.attr=T)
Finally, we can use this new data to add the 95% Confidence Intervals to the previous plot using again the plotCIF
function.
plotCIF(cifobj=mydat.CIF, maxtime= 20, ci=ciCIF)
npcrest
The package also offers a wrapper function (npcrest
) to do all these analyses in one step.
npcrest(df=data, exit=exit, event=event, exposure=b1nb0,rep=100, maxtime=20, print.attr=T)
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