荟萃分析：使用元包的摘要估计森林图

Question

我发现有一种方法可以使用“metafor”包创建摘要估计的森林图，可以在这里找到：元分析：使用metafor包的摘要估计的森林图

meta

包也有解决方案吗？经过 30 多项研究，

byvar

函数生成的森林图不适合窗口。

Answer 1

这里有一个例子：

### the data

d <- structure(list(study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 
7L, 8L, 8L, 8L, 10L, 9L, 11L, 11L, 12L, 13L, 14L, 14L, 15L, 16L, 
17L, 18L), .Label = c("Bort et al. 2012                ", "Boyl et al. 2004                ", 
"Cart et al. 2007                ", "Coryet al. 2009                 ", 
"Cosoff 1998                     ", "Dell'a 2011                     ", 
"Dilan 2003                      ", "Dilton et al. 1997              ", 
"Mac et al. 2001                 ", "Man et al. 2006                 ", 
"Okan 2011                       ", "Orol et al. 2006                ", 
"Pinto et al. 2003               ", "Simone et al. 2004              ", 
"Strahowski et al. 1992          ", "Tamara 2002                     ", 
"Viera et al. 2001               ", "Zucchi et al. 2006              "
), class = "factor"), xi = c(60, 40, 13, 107, 3, 32, 1, 16, 33, 
1, 20, 46, 27, 30, 22, 78, 35, 33, 5, 2, 4, 3, 4), ni = c(200, 
140, 56, 427, 20, 508, 25, 19, 53, 32, 44, 191, 288, 50, 46, 
918, 151, 360, 115, 41, 70, 129, 80), group = structure(c(3L, 
3L, 1L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 
1L, 2L, 1L, 1L, 4L, 4L), .Label = c("anxiety     ", "depression  ", 
"mixed       ", "remission   "), class = "factor")), .Names = c("study", 
"xi", "ni", "group"), row.names = c(NA, -23L), class = "data.frame", codepage = 1252L)

attach(d)

### the code

### load the library

library(meta)

library(metafor)

#### the model 
#### Freeman-Tukey Double arcsine transformation
#### Empirical Bayes estimator  and Hartung and Knapp adjustment

model <- metaprop(xi,ni,sm="PFT",hakn=TRUE, method.tau="EB")

########################
#
# subgroup analyses
#
########################


modelsub <- update(model, byvar=group)

summary(modelsub)

forest(modelsub,studlab=paste(study), print.byvar=FALSE)

#################################
#
# solution to a poor forest plot
#
#################################

### a good solution is to save the plot in .pdf 
### you have to play around with the 'width' and 'height' parameters 
### I used  width=12,height=13 (as higher the values, as more space you will get)

pdf(file="good forest plot.pdf", width=12,height=13)

forest(modelsub,studlab=paste(study), print.byvar=FALSE)

dev.off()