Author: Ng D, Antiporta DA, Matheson M, Munoz A

# Introduction to hrcomprisk

#### Ng D, Antiporta DA, Matheson M, Munoz A

# 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 version 0.1.0 of `hrcomprisk` from Github with:

    library(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) #varible 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:

1. The Cumulative Incidence of the both events of interest overall and by exposure level, and
2. The ratios of Hazard rations (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 this 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

1. 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 (in press)
2. 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