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:
remotes::install_github("ShixiangWang/metawho")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)
#> Loading required package: metafor
#> Loading required package: Matrix
#> Loading 'metafor' package (version 2.0-0). For an overview
#> and introduction to the package please type: help(metafor).
### 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)
#> entry trial subgroup hr ci.lb ci.ub conf_q
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 1.959964
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 1.959964
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 1.959964
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 1.959964
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 1.959964
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 1.959964
#> yi sei
#> 1 -1.2039728 0.6142831
#> 2 -2.2072749 0.8500678
#> 3 0.2231436 0.2143674
#> 4 -0.4620355 0.2082200
#> 5 -0.1053605 0.4030005
#> 6 -1.2729657 0.4387290Here we load example data.
library(metawho)
data("wang2019")
wang2019
#> entry trial subgroup yi sei
#> 1 Rizvi 2015-Male Rizvi 2015 Male -1.2039728 0.6142718
#> 2 Rizvi 2015-Female Rizvi 2015 Female -2.2072749 0.8500522
#> 3 Rizvi 2018-Male Rizvi 2018 Male 0.2231436 0.2143635
#> 4 Rizvi 2018-Female Rizvi 2018 Female -0.4620355 0.2082161
#> 5 Hellmann 2018-Male Hellmann 2018 Male -0.1053605 0.4029931
#> 6 Hellmann 2018-Female Hellmann 2018 Female -1.2729657 0.4387209Use deft_do() function to obtain model results.
# The 'Male' is the reference
(res = deft_do(wang2019, group_level = c("Male", "Female")))
#> $all
#> $all$data
#> entry trial subgroup yi sei
#> 1 Rizvi 2015-Male Rizvi 2015 Male -1.2039728 0.6142718
#> 2 Rizvi 2015-Female Rizvi 2015 Female -2.2072749 0.8500522
#> 3 Rizvi 2018-Male Rizvi 2018 Male 0.2231436 0.2143635
#> 4 Rizvi 2018-Female Rizvi 2018 Female -0.4620355 0.2082161
#> 5 Hellmann 2018-Male Hellmann 2018 Male -0.1053605 0.4029931
#> 6 Hellmann 2018-Female Hellmann 2018 Female -1.2729657 0.4387209
#>
#> $all$model
#>
#> Fixed-Effects Model (k = 6)
#>
#> Test for Heterogeneity:
#> Q(df = 5) = 18.8872, p-val = 0.0020
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.3207 0.1289 -2.4883 0.0128 -0.5732 -0.0681 *
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#>
#> $subgroup
#> $subgroup$data
#> trial hr ci.lb ci.ub conf_q yi
#> 1 Hellmann 2018 0.3111111 0.09679207 0.9999798 1.959964 -1.167605
#> 2 Rizvi 2015 0.3666667 0.04694148 2.8640863 1.959964 -1.003302
#> 3 Rizvi 2018 0.5040000 0.28058020 0.9053240 1.959964 -0.685179
#> sei
#> 1 0.5957176
#> 2 1.0487700
#> 3 0.2988405
#>
#> $subgroup$model
#>
#> Fixed-Effects Model (k = 3)
#>
#> Test for Heterogeneity:
#> Q(df = 2) = 0.5657, p-val = 0.7536
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.7956 0.2589 -3.0738 0.0021 -1.3030 -0.2883 **
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#>
#> attr(,"class")
#> [1] "deft"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.
- 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.
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