Hi Jordi,

if rmeta does not work in this way, try metafor for R instead. As Babak was describing, you can feed the Odds Ratios to the rma function directly.

You can also, and perhaps more effectively, first use the 'escalc' function of metafor to feed the observed adjusted Odds Ratios and standard errors/variance to the rma functions.

data

Robert Suurmond Babak Saravi Thank you both for your help! I have made some forest plots. Now working on their layout.

Dear Robert Suurmond Babak Saravi,

I hope I can bother you again with another question. I'd like to illustrate this with an example.

Data extraction

Study 1: aOR 0.94 95% CI [0.90-0.97]

Study 2: aOR 0.838 95% CI [0.73-0.96]

Study 3: aOR 0.952 95% CI [0.92-0.99]

Study 4: aOR 0.95 95% CI [0.92-0.99]

Study 5: aOR 0.90 95% CI [0.50-0.99]

Study 6: aOR 0.98 95% CI [0.95-1.01]

Study 7: aOR 1.04 95% CI [0.91-1.18]

Study 8: aOR 0.63 95% CI [0.58-0.92]

Because the standard error or variance was not reported I calculated this with the following code:

AGE_OR

I'd love to help you out, Jordi de Winkel . My response might be delayed, but could you send this by email instead?

Robert Suurmond thank you so much for your swift reply.

I have sent an email to your Maastricht University email.

I use the "meta" package from the R program

The following link may be useful

https://youtube.com/playlist?list=PLiTAgZ7ABwgVe6utn0wsKGmTu5-okUSeU

I found this code useful for my study.

library(Greg)

printCrudeAndAdjustedModel(model)