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.
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)
#> 载入需要的程辑包：metafor
#> 载入需要的程辑包：Matrix
#> Loading 'metafor' package (version 2.1-0). 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)
### 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
)
deft_prepare(wang2019)
#> entry trial subgroup hr ci.lb ci.ub ni
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 16
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 18
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 118
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 122
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 37
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 38
#> conf_q yi sei
#> 1 1.959964 -1.2039728 0.6142831
#> 2 1.959964 -2.2072749 0.8500678
#> 3 1.959964 0.2231436 0.2143674
#> 4 1.959964 -0.4620355 0.2082200
#> 5 1.959964 -0.1053605 0.4030005
#> 6 1.959964 -1.2729657 0.4387290Here we load example data.
library(metawho)
data("wang2019")
wang2019
#> entry trial subgroup hr ci.lb ci.ub ni
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 16
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 18
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 118
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 122
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 37
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 38
#> conf_q yi sei
#> 1 1.959964 -1.2039728 0.6142831
#> 2 1.959964 -2.2072749 0.8500678
#> 3 1.959964 0.2231436 0.2143674
#> 4 1.959964 -0.4620355 0.2082200
#> 5 1.959964 -0.1053605 0.4030005
#> 6 1.959964 -1.2729657 0.4387290Use 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
#> 1 Rizvi 2015-Male Rizvi 2015 Male 0.30 0.09 1.00 16
#> 2 Rizvi 2015-Female Rizvi 2015 Female 0.11 0.02 0.56 18
#> 3 Rizvi 2018-Male Rizvi 2018 Male 1.25 0.82 1.90 118
#> 4 Rizvi 2018-Female Rizvi 2018 Female 0.63 0.42 0.95 122
#> 5 Hellmann 2018-Male Hellmann 2018 Male 0.90 0.41 1.99 37
#> 6 Hellmann 2018-Female Hellmann 2018 Female 0.28 0.12 0.67 38
#> conf_q yi sei
#> 1 1.959964 -1.2039728 0.6142831
#> 2 1.959964 -2.2072749 0.8500678
#> 3 1.959964 0.2231436 0.2143674
#> 4 1.959964 -0.4620355 0.2082200
#> 5 1.959964 -0.1053605 0.4030005
#> 6 1.959964 -1.2729657 0.4387290
#>
#> $all$model
#>
#> Fixed-Effects Model (k = 6)
#>
#> 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
#> 1 Rizvi 2015 0.3666667 0.0469397 2.8641945 34 1.959964 -1.003302
#> 2 Rizvi 2018 0.5040000 0.2805772 0.9053338 240 1.959964 -0.685179
#> 3 Hellmann 2018 0.3111111 0.0967900 1.0000013 75 1.959964 -1.167605
#> sei
#> 1 1.0487893
#> 2 0.2988460
#> 3 0.5957285
#>
#> $subgroup$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 -3.0737 0.0021 -1.3030 -0.2883 **
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#>
#> attr(,"class")
#> [1] "deft"For visualization and more, see package vignette.
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.
