漏斗图 (metafor)

作者

[编辑] 郑虎;

[审核] .

可以用于分析 Meta 分析结果中潜在偏倚因子。

环境配置

系统: Cross-platform (Linux/MacOS/Windows)
编程语言: R
依赖包: data.table; jsonlite; metafor; ggplotify

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("metafor", quietly = TRUE)) {
  install.packages("metafor")
}
if (!requireNamespace("ggplotify", quietly = TRUE)) {
  install.packages("ggplotify")
}

# 加载包
library(data.table)
library(jsonlite)
library(metafor)
library(ggplotify)

数据准备

# 加载数据
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/funnel-plot-metafor/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# 整理数据格式
data2 <- escalc(ri=ri, ni=ni, data = data, measure="ZCOR")
res <- rma(yi, vi, data = data2)

# 查看数据
head(data)

              authors year  ni    ri controls          design   a_measure
1     Axelsson et al. 2009 109 0.187     none cross-sectional self-report
2     Axelsson et al. 2011 749 0.162     none cross-sectional self-report
3        Bruce et al. 2010  55 0.340     none     prospective       other
4  Christensen et al. 1999 107 0.320     none cross-sectional self-report
5 Christensen & Smith 1995  72 0.270     none     prospective       other
6        Cohen et al. 2004  65 0.000     none     prospective       other
  c_measure meanage quality
1     other   22.00       1
2       NEO   53.59       1
3       NEO   43.36       2
4     other   41.70       1
5       NEO   46.39       2
6       NEO   41.20       2

可视化

# 漏斗图
p <- as.ggplot(function(){
  funnel(x = res, main = "Funnel Plot (metafor)",
         level = c(90, 95, 99), shade = c("white","#a90e07","#d23e0b"), refline = 0)
  })

p