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 stable release of metawho from CRAN with:
You can install the development version of metawho from GitHub with:
Visualization feature of metawho needs the recent version of forestmodel, please run the following commands:
Example
This is a basic example which shows you how to solve a common problem.
Prepare data
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)
### specify sample number
ni <- c(16L, 18L, 118L, 122L, 37L, 38L)
### 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,
ni = ni,
stringsAsFactors = FALSE
)
wang2019 = deft_prepare(wang2019)Here we can directly load example data.
library(metawho)
data("wang2019")
wang2019
#> entry trial subgroup hr ci.lb ci.ub ni conf_q
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 16 1.959964
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 18 1.959964
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 118 1.959964
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 122 1.959964
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 37 1.959964
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 38 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 0.4387290Do deft analysis
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 hr ci.lb ci.ub ni conf_q
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 16 1.959964
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 18 1.959964
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 118 1.959964
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 122 1.959964
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 37 1.959964
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 38 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
#>
#> $all$model
#>
#> Fixed-Effects Model (k = 6)
#>
#> I^2 (total heterogeneity / total variability): 73.53%
#> H^2 (total variability / sampling variability): 3.78
#>
#> Test for Heterogeneity:
#> Q(df = 5) = 18.8865, p-val = 0.0020
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.3207 0.1289 -2.4883 0.0128 -0.5733 -0.0681 *
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#>
#> $subgroup
#> $subgroup$data
#> trial hr ci.lb ci.ub ni conf_q yi sei
#> 1 Rizvi 2015 0.3666667 0.0469397 2.8641945 34 1.959964 -1.003302 1.0487893
#> 2 Rizvi 2018 0.5040000 0.2805772 0.9053338 240 1.959964 -0.685179 0.2988460
#> 3 Hellmann 2018 0.3111111 0.0967900 1.0000013 75 1.959964 -1.167605 0.5957285
#>
#> $subgroup$model
#>
#> Fixed-Effects Model (k = 3)
#>
#> I^2 (total heterogeneity / total variability): 0.00%
#> H^2 (total variability / sampling variability): 0.28
#>
#> 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 -3.0737 0.0021 -1.3030 -0.2883 **
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#>
#> attr(,"class")
#> [1] "deft"Show analysis result
Use deft_show() to visualize results.
To show all entries without model result.
To show result of subgroup analysis.
The analysis above reproduced Figure 5 of reference #1.
Combine with metafor
The result of deft_do() contains models constructed by
metafor package, so you can use features provided by
metafor package, e.g. plot the model results with
forest() function from metafor
package.
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)
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.