Output of metafor for incidence rates I need to clarify something and am hoping Wolfgang or someone else with expertise in the metafor R package could help.
I want to know what the output of the rma model (with a moderator in is for a meta-analysis of incidence rates)
i.e.
data_outcomes<- escalc(measure = "IR", xi = xi, ti = ti, data = data_outcomes)
res <- rma(yi, vi, data = data_outcomes, mods=~medicine_1)

gets the output:
Mixed-Effects Model (k = 21; tau^2 estimator: REML)

  logLik  deviance       AIC       BIC      AICc  
  40.1901  -80.3802  -74.3802  -71.5469  -72.7802  

tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0001)
tau (square root of estimated tau^2 value):             0.0047
I^2 (residual heterogeneity / unaccounted variability): 3.83%
H^2 (unaccounted variability / sampling variability):   1.04
R^2 (amount of heterogeneity accounted for):            57.62%

Test for Residual Heterogeneity: 
QE(df = 19) = 13.0130, p-val = 0.8379

Test of Moderators (coefficient(s) 2): 
QM(df = 1) = 0.5839, p-val = 0.4448

Model Results:

              estimate      se    zval    pval    ci.lb   ci.ub   
intrcpt        0.1361  0.0055  2.0449  0.0409   0.0005  0.0222  *
medicine1_good 0.0947  0.0103  0.7641  0.4448  -0.0124  0.0281   

What is the estimate for the moderator/covariate expressed as?
 A: The intercept (0.1361) is the estimated average incidence rate when medicine1_good is equal to zero and the coefficient for medicine1_good tells you how the estimated average incidence rate changes for a one-unit increase in medicine1_good.
A: yes this is a real output and no issues with copy pasting.
I suspect it's something amiss with my original data. However, here is a reproducible example below:
 #Example

study_id = 1:10
example_meta = data.frame(study_id)

example_meta$participants_n = round(seq(40, 244, length.out = 10),0)

example_meta$follow_up_time = round(seq(30, 365, length.out = 10),0)

example_meta$event_n =  round(seq(4, 60, length.out = 10),0)

example_meta$medicine_1 =  1:2

example_meta$medicine_1 = factor(example_meta$medicine_1)

#set ti and vi
example_meta$ti = example_meta$follow_up_time

example_meta$xi = example_meta$event_n


#Escalc
library(metafor)
data_calc = escalc(measure = "IR", xi = xi, ti = ti, data = example_meta)
rma(yi, vi, data = data_calc, mods=~medicine_1)

Output:
Model Results:

             estimate      se     zval    pval    ci.lb   ci.ub     
intrcpt        0.1608  0.0134  11.9902  <.0001   0.1345  0.1871  ***
medicine_12    0.0019  0.0182   0.1052  0.9162  -0.0337  0.0375     

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

