[R-meta] Queries re: rma.mv
Adelina Artenie
@de||n@@@rten|e @end|ng |rom br|@to|@@c@uk
Wed Apr 20 19:15:24 CEST 2022
Hello
I am trying to do a meta-regression to explore whether incidence rates are changing over time. Some of the estimates are correlated so I am using the rma.mv function. The moderator is a measure of time.
I have two questions please:
1. I am trying to understand why is it that the correlated estimates are systematically up-weighted. I would have thought that any correlated data would be down-weighted or, at the very least, have a similar weight as the uncorrelated data. I appreciate that the weight calculation in this case is complex (https://www.metafor-project.org/doku.php/tips:weights_in_rma.mv_models ). I wonder if, conceptually, this would be expected? In my mock example, the correlated estimates have weights of 7-10% whereas the uncorrelated ones have weights of 4%. I see a similar trend in my own data, and I find the difference in the weights to be quite considerable.
2. Could I confirm with you that my interpretation of the coefficient for the moderator is correct? In this case, the estimate is 0.0846, which represents the mean/median log IRR (incidence rate ratio) for a one-unit increase in the moderator. Therefore, the IRR is 1.088: for each unit increase in time, the incidence rate increases on average by 8.8% (ignoring the confidence intervals for now).
Many thanks in advance,
Adelina
Adelina Artenie, MSc, PhD
CIHR Postdoctoral Research Fellow
Population Health Sciences
Bristol Medical School
University of Bristol
Code
***
ID <- c(1:18)
author <- c("AA", "AA", "BB", "CC", "DD", "EE", "EE", "EE", "FF", "GG", "HH", "II", "JJ",
"KK", "LL", "MM", "NN", "OO")
cases <- c(8:25)
prs_yrs <-c(200,150,3000,400,500,100,400,300,1200,456,177,296,664,123,123,432,67,3045)
moderator<-c(1:18)
mydata<-data.frame(ID,author,cases,prs_yrs,moderator)
print(mydata)
my_example <- escalc(measure="IRLN", xi=cases, ti=prs_yrs, data=mydata)
my_example
my_meta_reg <- rma.mv(yi = yi,
V = vi,
slab = author,
data = my_example,
random = ~ 1 | author/ID,
test = "z",
method = "REML",
mods = ~ moderator)
summary(my_meta_reg)
regplot(my_meta_reg, label = T, transf=exp,
ylab = "Incidence rate",
xlab = "Moderator",
col="black",
plim = c(1,NA),
labsize = 0.5,
legend = F)
forest(my_meta_reg, showweights=TRUE)
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