# 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.