# MonotoneHazardRatio

## Overview

MonotoneeHazardRatio is a tool for nonparametric estimation and inference for a monotone non-decreasing hazard ratio, based on the work “Nonparametric inference under a monotone hazard ratio order” by Y. Wu and T. Westling (2022) <arXiv:2205.01745>.

## Dependent packages

Our packages needs the following packages to work.

```
library(survival)
library(fdrtool)
library(KernSmooth)
library(twostageTE)
```

## Usage

It is staightforward to use this package. First you need to import the data (optional: split the data into two groups “S” and “T” such that the hazard ratio \(\lambda_S/\lambda_T\) is non-decreasing). Pass the dataframes along with the evaluation grid to the function `monotoneHR()`

, which takes \(\alpha =0.05\) as the default confidence level, to have the hazard ratio and its confidence intervals estimated.

## Example

As shown in the example, we are going to estimate a non-decreasing hazard ratio using the example data `survData`

. The estimated hazard ratio is stored in `theta$hr`

, while the confidence intervals are stored in `theta$ci.lower`

and `theta$ci.upper`

.

```
library(MonotoneHazardRatio)
### Use the example data in the package
data(survData)
### split it into two dataframes
s.data <- survData[survData$group == 'S']
t.data <- survData[survData$group == 'T']
### Evaluation grid
t.grid <- seq(0, 10, 1)
### Estimation and inference
theta <- monotoneHR(t.grid, s.data, t.data)
```