---
title: "Introduction to hrcomprisk"
author: "Ng D, Antiporta DA, Matheson M, Munoz A"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Vignette_1}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  fig.width = 10,
  fig.height = 8
)
```

# hrcomprisk: Nonparametric Assessment of Differences Between Competing Risks Hazards

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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](https://github.com) with:

``` r
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](https://statepi.jhsph.edu/ckid/), wich has the necessary variables to run the package.

```{r example}
library(hrcomprisk)
data <- hrcomprisk::dat_ckid
dim(data) #dimensions
names(data) #varible 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.

```{r CRCumInc_example}
mydat.CIF<-CRCumInc(df=data, time=exit, event=event, exposed=b1nb0, print.attr=T)
```

## 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.

```{r plotCIF}
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:
```{r boot_example}
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.

```{r plot_ci}
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.

```{r npcrest}
npcrest(df=data, exit=exit, event=event, exposure=b1nb0,rep=100, maxtime=20, print.attr=T)
```

## 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](https://link.springer.com/chapter/10.1007/978-1-4614-8981-8_1)
