I am completing a Systematic Review of interventions for my MA thesis. The main focus of my thesis was going to be a meta-analysis. I have 5 articles that meet my inclusion criteria. Only one study contains data necessary for a meta-analysis. Two of these studies use multi-level modelling, and do not provide group Standard Deviations(SD). My concern with using Standard Estimates (SE) rather SD, or even converting the SE to SD myself is confounding variables. Is there a was to include this in a meta-analysis without original data or SD's? Because I don't know specifically what the original researchers would have statistically controlled for different variables across studies, would it make sense to include them or would it be better to use a narrative synthesis method to summarize the data rather than comparing overall effect sizes? I know a meta-analysis is only as good as the data included and I have to make sure that I am contributing to my field in a meaningful way rather that inflating (or circulating inaccurate information). Any information and/or articles you could give me on pooling effects across studies or using SE in meta-analysis.
Hi,
I am not sure if I have understood your query correctly.
One of the methods to pool the results of individual studies in a meta-analysis is the generic inverse variance method which actually uses the standard error (SE) and the effect sizes like odds ratio/relative risk. It is preferable to use this method if the studies have provided the adjusted effect sizes and SE (than using the unadjusted effect size estimated using the proportion with the outcomes of interest in the two groups).
If the outcome of interest is a continuous variable, you can still use the adjusted mean difference along with the 95% CI or the standard error. There is no need to use the group means and SD (in fact the former approach using the adjusted mean difference and 95% CI is better).
Hope this clarifies your query.
Jeeva
Hi Priscilla,
In terms of counfounding it makes no difference whether you use standard deviation (SD) or standard error (SE). I am not sure what you mean by standard estimate?
You can easily convert standard deviation to standard error: SD = SE x squar root of sample size
Thank you everyone for your responses. That have all been very helpful. I did mean standard error for SE.
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