knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The goal of metawho is to provide simple R implementation of "Meta-analytical method to Identify Who Benefits Most from Treatments" (called 'deft' approach, see reference #2).
metawho is powered by R package metafor and does not support dataset contains individuals for now. Please use stata package ipdmetan if you are more familar with stata code.
You can install the development version of metawho from GitHub with:
This is a basic example which shows you how to solve a common problem.
If you have HR and confidence intervals, please run
library(metawho) ### specify hazard ratios (hr) hr <- c(0.30, 0.11, 1.25, 0.63, 0.90, 0.28) ### specify lower bound for hr confidence intervals ci.lb <- c(0.09, 0.02, 0.82, 0.42, 0.41, 0.12) ### specify upper bound for hr confidence intervals ci.ub <- c(1.00, 0.56, 1.90, 0.95, 1.99, 0.67) ### trials trial <- c("Rizvi 2015", "Rizvi 2015", "Rizvi 2018", "Rizvi 2018", "Hellmann 2018", "Hellmann 2018") ### subgroups subgroup = rep(c("Male", "Female"), 3) entry <- paste(trial, subgroup, sep = "-") ### combine as data.frame wang2019 = data.frame( entry = entry, trial = trial, subgroup = subgroup, hr = hr, ci.lb = ci.lb, ci.ub = ci.ub, stringsAsFactors = FALSE ) deft_prepare(wang2019)
Here we load example data.
library(metawho) data("wang2019") wang2019
deft_do() function to obtain model results.
# The 'Male' is the reference (res = deft_do(wang2019, group_level = c("Male", "Female")))
Plot the model results with
forest() function from metafor package.
forest(res$subgroup$model, showweights = TRUE)
Modify plot, more see
forest(res$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.
- 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.