library(meta)

pasi75 <- read.csv('pasi75.csv', header = T)

rate.75 <- transform(pasi75, p = Events/Total)

shapiro.test(rate.75$p) # 原始率的正态检验，如果p-value<0.05则说明不符合正态分布，需要转换

meta.pasi75 <- metaprop(Events, Total, data= pasi75, studlab = pasi75$Study

            ,sm="PRAW", 

            subgroup = Endpoint, overall = F)

pdf("PASI75.pdf",height = 9,width = 10)

forest(meta.pasi75, layout = "Revman",

    leftcols = c("studlab","event","n","effect.ci","w.random"),

    rightcols = F,

    digits=3, digits.I2 = 2, digits.tau2 = 3, digits.pval =3,

    family="sans",

    fontsize=9.5,lwd=2, 

    col.diamond.fixed="lightslategray",#固定效应菱形颜色

    col.diamond.lines.fixed="lightslategray", #固定效应菱形线条颜色

    col.diamond.random="#669999",#​随机效应菱形颜色

    col.diamond.lines.random="maroon",#随机效应菱形线条颜色

    col.square="#336699",#​ 反映权重的方块颜色

    col.square.lines = "#FF99FF",#​ 方块框颜色

    col.study="#006666",#​ 横线置信区间颜色

    col.inside = "FF0033",#如果置信限完全在方块内，个体研究结果和置信限的颜色

    col.label.right = "FF0033", # 

    col.random = "#660099",

    col.subgroup = "#003399",

    lty.fixed=4,

    plotwidth="8cm",

    colgap.forest.left="0.5cm",

    colgap.forest.right="1cm",

    just.forest="right",

    colgap.left="0.5cm",

    colgap.right="0.5cm", 

    common = F, #是否统计固定效应模型

    xlab = "Rate of achieving PASI75", #x轴标签

    xlim = c(0,1),

    ref = 0,# 参考线的横坐标

    squaresize = 1.6, w.random = T,

    smlab = "Proportion",

    sep.subgroup =":")

dev.off()