I am doing meta-analysis using the metafor package and I have the following data structure(used this example from Wolfgang's answer):
I have only one observation within each group. I have used the three-level model to fit the logit transformed proportions and have used the mean age as the predictor. Following is the code and output:
res <- rma.mv(yi, vi, mods = ~ mean.age, random = ~ 1 | study/group, data=dat) Multivariate Meta-Analysis Model (k = 14; method: REML) Variance Components: estim sqrt nlvls fixed factor sigma^2.1 0.0000 0.0000 10 no study sigma^2.2 0.3752 0.6125 14 no study/group Test for Residual Heterogeneity: QE(df = 12) = 39.8526, p-val < .0001 Test of Moderators (coefficient 2): QM(df = 1) = 1.0301, p-val = 0.3101 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.3130 1.2710 0.2463 0.8055 -2.1781 2.8041 mean.age -0.0494 0.0486 -1.0149 0.3101 -0.1447 0.0460 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Can anyone please help me with the interpretation of the coefficient of the mean age variable? My understanding is that the average log odds of passing decrease by 0.05 with the unit increase in the mean age. In my data, the proportions that I have for each group within each study are assumed to have a sigmoidal association with the quantitative variable in the model so I was wondering if the multilevel model would be able to model this association correctly?