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I would like to conduct a meta-analysis on a set of studies that have measured an outcome over a period prior to intervention and post-intervention for a control group and experimental group. A method of doing this for quantitative data was demonstrated by Morris (2008) and can be conducted in the Metafor package in R.

The only problem with Morris's proposed effect size is that it seems to be designed around the standardised mean change, which is likely not appropriate for count data. I understand that I can use the incident rate ratio (IRR) or the rate difference for count data to look at the differences between two groups. But, what if like Morris, I want to look at differences pre and post for my treatment group compared with differences pre and post for my control.

One option would be to calculate rate differences between pre and post for both groups and then to subtract one from the other. But, I am not sure whether the likely correlation between the pre and post results would make my effect size inaccurate. Another would be to potentially use the control group rate difference as a moderator, but this seems to have the same problem.

Does anyone have any suggestions on how to deal with this issue?

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  • $\begingroup$ "whether the likely correlation between the pre and post results would make my effect size " How do I estimate the correlti between pre and post ? Your is a count data ? $\endgroup$ – Subhash C. Davar Jun 14 at 16:24
  • $\begingroup$ It is. Why can't I estimate correlations with count data? This is done pretty regularly...see stats.stackexchange.com/questions/276360/… $\endgroup$ – John Jun 14 at 16:28
  • $\begingroup$ does not answer my question. And what prompts you to think of correlation between pre and post ? $\endgroup$ – Subhash C. Davar Jun 14 at 16:37
  • $\begingroup$ Accounting for dependence between pre and post measures on the same group. You can estimate the correlation with Pearson's r... $\endgroup$ – John Jun 14 at 16:39
  • $\begingroup$ The difference in proportion for the pre and post treatment (exp. design) can be used to estimate the effect-size of a study. Why should we compute and compare it with difference in control group (pre and post treatment). To me, doing so itself results in a true estimate of effectsize which does not serve as a good basis for meta analysis. You need not land into alley of difference being measured on continuous scale. $\endgroup$ – Subhash C. Davar Jun 15 at 9:21

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