# metawho

```
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```

`library(metawho)`

The goal of **metawho** is to provide simple R implementation of "Meta-analytical method to Identify Who Benefits Most from Treatments" (called 'deft' approach, see reference #2).

**metawho** is powered by R package **metafor** and does not support dataset contains individuals for now. Please use stata package **ipdmetan** if you are more familar with stata code.

## Installation

You can install the development version of **metawho** from GitHub with:

`remotes::install_github("ShixiangWang/metawho")`

## Example

This is a basic example which shows you how to solve a common problem.

If you have HR and confidence intervals, please run `deft_prepare()`

firstly.

```
library(metawho)
### specify hazard ratios (hr)
hr <- c(0.30, 0.11, 1.25, 0.63, 0.90, 0.28)
### specify lower bound for hr confidence intervals
ci.lb <- c(0.09, 0.02, 0.82, 0.42, 0.41, 0.12)
### specify upper bound for hr confidence intervals
ci.ub <- c(1.00, 0.56, 1.90, 0.95, 1.99, 0.67)
### trials
trial <- c("Rizvi 2015", "Rizvi 2015",
"Rizvi 2018", "Rizvi 2018",
"Hellmann 2018", "Hellmann 2018")
### subgroups
subgroup = rep(c("Male", "Female"), 3)
entry <- paste(trial, subgroup, sep = "-")
### combine as data.frame
wang2019 =
data.frame(
entry = entry,
trial = trial,
subgroup = subgroup,
hr = hr,
ci.lb = ci.lb,
ci.ub = ci.ub,
stringsAsFactors = FALSE
)
deft_prepare(wang2019)
```

Here we load example data.

```
library(metawho)
data("wang2019")
wang2019
```

Use `deft_do()`

function to obtain model results.

```
# The 'Male' is the reference
(res = deft_do(wang2019, group_level = c("Male", "Female")))
```

Plot the model results with `forest()`

function from **metafor** package.

`forest(res$subgroup$model, showweights = TRUE)`

Modify plot, more see `?forest.rma`

.

```
forest(res$subgroup$model, showweights = TRUE, atransf = exp,
slab = res$subgroup$data$trial,
xlab = "Hazard ratio")
op = par(no.readonly = TRUE)
par(cex = 0.75, font = 2)
text(-11, 4.5, "Trial(s)", pos = 4)
text(9, 4.5, "Hazard Ratio [95% CI]", pos = 2)
par(op)
```

This reproduce Figure 5 of reference #1. Of note, currently **metawho** only support HR values. More usage about model fit, prediction and plotting please refer to metafor package.

## References

- Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).
- Fisher, David J., et al. "Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?." bmj 356 (2017): j573.