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
#> [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"
```

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)
```

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)
```

`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"
```

- 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

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