![]() ![]() dmres<-rma.uni(yi=logor, sei=se, data=dm2)įorest(dmres, atransf=exp, slab=paste(dm2$author)) An odds ratio is meaningful at any prevalence, but a risk ratio can produce estimates of risk that are greater than 100 in cases where the baseline prevalence is high enough. However when I fit the model and plot the forest plot, the resulting confidence intervals differ quite a bit from the ones I started with. My solution was to take the average of these as the standard errors in the model. I think this is due to the fact that the CI's were rounded by the authors. My problem is, the standard errors derived in this way differ a little bit, although they should be the same. For example, an odds ratio of 1.2 is above 1.0, but is not a strong association. The further away an odds ratio is from 1.0, the more likely it is that the relationship between the exposure and the disease is causal. So I calculated the standard errors in the following way (logor = log(odds ratio), UL= CI upper limit, LL = CI lower limit): se1<-(log(UL)-logor)/1.96 The magnitude of the odds ratio is called the strength of the association. In order to use rma.uni() from the metafor package, I need to supply variances (through vi=" ") or standard errors (throuh sei = " "). The source articles do not report standard errors. I'm trying to do a meta-analysis from a number of Odds ratios and their confidence intervals. ![]()
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