I am working on a meta-analysis that compares studies testing the retentionenter image description here of different materials after different follow-ups. (each study tests one material for a certain amount of time). I first took the approach of grouping different materials with similar follow up times, and calculating a summary proportion of all materials, however I later found out that performing a moderator analysis ( in this case “materials”) would only compare the materials against the summary proportion (?), which is not my aim. I aim to see how each material performed, and compare directly between them. For example, I have materials A,B,C,D and I wish to compare the retention between A and B, to see if they are significantly different.

So I am thinking I should calculate a summary proportion for each material alone, and then perhaps conduct a chi square test? to see if the summary means of each material is different?

I would really appreciate any input. Attached is the summary proportion of all materials reporting retention after 3 years. For my paper, I want to highlight that the GI material is significantly worse than the Auto material. Could I still do that with moderator analysis?


1 Answer 1


Your interpretation of what a moderator analysis would do may be correct if you have set up the contrasts appropriately. If you are using the defaults it is not what they do. Bear in mind that if you have $k$ levels of a factor then there are only $k-1$ independent comparisons so doing all pair-wise needs careful thought and interpretation.

You analyses seem to reveal that you have a high degree of heterogeneity as far as I can tell although your plots are barely readable at least on my screen. You might like to investigate that heterogeneity first as the random effects model does not estimate a single overall effect size but rather the mean and variance of the distribution of effect sizes. Your suggestion of computing a summary proportion would be averaging things which your data-set suggests are different.


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