漏斗图

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: FunnelPlotR; gridExtra

# 安装包
if (!requireNamespace("FunnelPlotR", quietly = TRUE)) {
  install.packages("FunnelPlotR")
}
if (!requireNamespace("gridExtra", quietly = TRUE)) {
  install.packages("gridExtra")
}

# 加载包
library(FunnelPlotR)
library(gridExtra)

数据准备

# 加载数据
data <- read.delim("files/Hiplot/058-funnel-plot-data.txt", header = T)

# 查看数据
head(data)

  los hmo white died age80 type type1 type2 type3 provnum     prds
1   4   0     1    0     0    1     1     0     0   30001 9.667315
2   9   1     1    0     0    1     1     0     0   30001 8.956472
3   3   1     1    1     1    1     1     0     0   30001 6.856678
4   9   0     1    0     0    1     1     0     0   30001 9.667315
5   1   0     1    1     1    1     1     0     0   30001 7.400868
6   4   0     1    1     0    1     1     0     0   30001 7.561051

可视化

# 漏斗图
plot_cols <- c("#925E9FFF","#FDAF91FF","#AD002AFF","#ADB6B6FF","#00468BFF","#ED0000FF","#42B540FF","#0099B4FF")
p <- funnel_plot(
  data, numerator = los, denominator = prds,  group = provnum, data_type = "SR",
  limit = 99, label = "outlier", sr_method = "SHMI", trim_by=0.1, 
  title = "Funnel Plot", x_range = "auto", y_range = "auto",
  plot_cols=plot_cols
  )

p

A funnel plot object with 54 points of which 9 are outliers. 
Plot is adjusted for overdispersion.