hrcomprisk: Nonparametric Assessment of Differences Between Competing Risks Hazards

This package aims to estimate Nonparametric Cumulative-Incidence Based Estimation of the Ratios of Sub-Hazard Ratios to Cause-Specific Hazard Ratios.

Installation

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")

Using a formatted data set to apply the `hrcomprsk` package

You 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
#> [1] 626  13
names(data) #variable names
#>  [1] "b1nb0"        "event"        "male1fe0"     "incomelt30"   "incomegt75"  
#>  [6] "lps"          "foodassist"   "public"       "matedultcoll" "privatemd"   
#> [11] "entry"        "exit"         "inckd"

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)
#> $names
#>  [1] "event"       "exposure"    "time"        "CIoinc_comp" "CIxinc_comp"
#>  [6] "CIoinc_1"    "CIxinc_1"    "CIoinc_2"    "CIxinc_2"    "R1"         
#> [11] "R2"         
#> 
#> $class
#> [1] "data.frame"

Using a the output to create Plots of CIFs and the Ratio of Hazard Ratios (Rk)

We can also obtain two different plots using the plotCIF function:

The Cumulative Incidence of both events of interest, overall and by exposure level, and
The ratios of Hazard ratios (sub-distribution Hazard Ratio and cause-specific Hazard Ratio) by event.

plots<-plotCIF(cifobj=mydat.CIF, maxtime = 20, eoi = 1)

Bootstrapping the data to get 95% Confidence Intervals for the Ratio of Hazard Ratios (Rk)

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)
#> $names
#> [1] "R1.lower" "R1.upper" "R2.lower" "R2.upper"
#> 
#> $class
#> [1] "data.frame"

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)

The wrapper function `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)
#> $names
#>  [1] "event"       "exposure"    "time"        "CIoinc_comp" "CIxinc_comp"
#>  [6] "CIoinc_1"    "CIxinc_1"    "CIoinc_2"    "CIxinc_2"    "R1"         
#> [11] "R2"         
#> 
#> $class
#> [1] "data.frame"
#> 
#> $names
#> [1] "R1.lower" "R1.upper" "R2.lower" "R2.upper"
#> 
#> $class
#> [1] "data.frame"

References

Ng D, Antiporta DA, Matheson M, Munoz A. Nonparametric assessment of differences between competing risks hazard ratios: application to racial differences in pediatric chronic kidney disease progression. Clinical Epidemiology, 2020. Link to Journal
Muñoz A, Abraham AG, Matheson M, Wada N. In: Risk Assessment and Evaluation of Predictions. Lee MLT, Gail M, Pfeiffer R, Satten G, Cai T, Gandy A, editor. New York: Springer; 2013. Non-proportionality of hazards in the competing risks framework; pp. 3–22. Google Scholar