I'm trying to understand how to perform meta-regression treating each
arm of studies in my data as an individual study with a single
proportion as the outcome. I have read through the documentation for
the R package metafor, but have only found the option to estimate
using relative risk, odds ratio or other similar measures for two-arm
studies. I have also found this function metaprop in the package meta
(https://rdrr.io/cran/meta/man/metaprop.html) that seems it might be a
good option, but I cannot see how to perform the regression against a
predictor. Any other R package suggestions?
EDIT: Thanks to guidance from mdewey and dbwilson, I have come to understand the possible models and code used to create said models. In the analyses below I am using the function rma.mv.
As the interest in my analysis is in the effect of the variable plateau pressure (PP from now on), I would like to include it in a meta-regression model with mortality rate as the outcome. Data come from 25 studies each with 2 arms, but as there is a strongly significant difference in PP between the two arms, I do not believe both variables should be included in the model. However, as the results of each arm of a given study are clearly not independent it seems to me that a way to account for this would be to include the Study id variable (1,1,2,2,…25,25) as a random effect. However, when I first had run the model with Overall id (1,2….50) as the random effect, the effect estimate of PP was approx. 0.01, p<0.0001, and when changing the random effect to Study id, the effect of PP was 0.0038, p = 0.119. I am unable to understand the reason for this change; under the model with Overall id as a random effect each observation would have a different estimated random effect, but in the model with Study id as a random effect the estimated random effect would be the same within each study for the two arms. I do not understand then why there would be a dramatic change in the significance of the fixed effect PP, due to the random effect being estimated on a per study base, rather than an individual basis. If anyone has insight on why this change may be occurring it would be very helpful and appreciated.
Best Answer
You should use the logit ($ln\left(\frac{p}{1-p}\right)$) and as the effect size index. Along with the inverse variance weight, you can then use rma.uni function in metafor to perform meta-analytic regression. This function can be used with any generic effect size index and its associated variance or standard error (for the inverse variance weight).