metafor 提供与原始值不同的 95% CI
我正在使用 metafor 包来组合线性回归模型中的 beta 系数。我使用了以下代码。我提供了 rma 函数报告的 se 和 beta 值。但是，当我看到森林图时，95% 置信区间与研究中报告的不同。我还尝试使用 mtcars 数据集运行三个模型并组合系数。尽管如此，我们在森林图上看到的 95% CI 仍与原始模型不同。这些偏差远非舍入误差。下面是一个可重现的示例。
library(metafor)
library(dplyr)
lm1 <- lm(hp~mpg, data=mtcars[1:15,])
lm2 <- lm(hp~mpg, data=mtcars[1:32,])
lm3 <- lm(hp~mpg, data=mtcars[13:32,])
study <- c("study1", "study2", "study3")
beta_coef <- c(lm1$coefficients[2],
lm2$coefficients[2],
lm3$coefficients[2]) %>% as.numeric()
se <- c(1.856, 1.31,1.458)
ci_lower <- c(confint(lm1)[2,1],
confint(lm2)[2,1],
confint(lm3)[2,1]) %>% as.numeric()
ci_upper <- c(confint(lm1)[2,2],
confint(lm2)[2,2],
confint(lm3)[2,2]) %>% as.numeric()
df <- cbind(study=study,
beta_coef=beta_coef,
se=se,
ci_lower=ci_lower,
ci_upper=ci_upper) %>% as.data.frame()
pooled <- rma(yi=beta_coef, vi=se, slab=study)
forest(pooled)
将森林图上的置信区间与数据框中的置信区间进行比较。
数据框
df <- cbind(study=study,
beta_coef=beta_coef,
se=se,
ci_lower=ci_lower,
ci_upper=ci_upper) %>% as.data.frame()
I am using metafor package for combining beta coefficients from the linear regression model. I used the following code. I supplied the reported se and beta values for the rma function. But, when I see the forest plot, the 95% confidence intervals are different from the ones reported in the studies. I also tried it using mtcars data set by running three models and combining the coefficients. Still, the 95%CI we see on the forest plot are different from the original models. The deviations are far from rounding errors. A reproducible example is below.
library(metafor)
library(dplyr)
lm1 <- lm(hp~mpg, data=mtcars[1:15,])
lm2 <- lm(hp~mpg, data=mtcars[1:32,])
lm3 <- lm(hp~mpg, data=mtcars[13:32,])
study <- c("study1", "study2", "study3")
beta_coef <- c(lm1$coefficients[2],
lm2$coefficients[2],
lm3$coefficients[2]) %>% as.numeric()
se <- c(1.856, 1.31,1.458)
ci_lower <- c(confint(lm1)[2,1],
confint(lm2)[2,1],
confint(lm3)[2,1]) %>% as.numeric()
ci_upper <- c(confint(lm1)[2,2],
confint(lm2)[2,2],
confint(lm3)[2,2]) %>% as.numeric()
df <- cbind(study=study,
beta_coef=beta_coef,
se=se,
ci_lower=ci_lower,
ci_upper=ci_upper) %>% as.data.frame()
pooled <- rma(yi=beta_coef, vi=se, slab=study)
forest(pooled)
Compare the confidence intervals on the forest plot with the one on the data frame.
data frame
df <- cbind(study=study,
beta_coef=beta_coef,
se=se,
ci_lower=ci_lower,
ci_upper=ci_upper) %>% as.data.frame()
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参数
vi用于指定抽样方差，但您将标准误差传递给参数。所以你应该这样做：但是你仍然会发现这里存在差异，因为森林图中的 CI 是基于正态分布构建的，而从回归模型获得的 CI 是基于 t 分布的。如果您想要森林图中完全相同的 CI，您可以将 CI 边界传递给如下函数：
如果您想将某些元分析中的摘要多边形添加到森林图中，您可以使用
addpoly()。所以这个例子的完整代码是：Argument
viis for specifying the sampling variances, but you are passing the standard errors to the argument. So you should do:But you will still find a discrepancy here, since the CIs in the forest plot are constructed based on a normal distribution, while the CIs you obtained from the regression model are based on t-distributions. If you want the exact same CIs in the forest plot, you could just pass the CI bounds to the function like this:
If you want to add a summary polygon from some meta-analysis to the forest plot, you can do this with
addpoly(). So the complete code for this example would be: