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)
#>                  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 0.4387290

Here 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 0.4387209

Use 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.

References