I'm doing meta-analysis for a treatment done in seeds. I have approximately 130 series with value of the control mean (Xc), its treated homologous mean (Xe) for crop yield and the number of replications (Nc and Ne) of each experiment which I'll use the number of replications as "sample size”. From those 130 studies, I got like 5% of them with diverse measures of variance: pooled standard deviation for control and treated, standard deviations, P values… (see the picture) that's why I think it’s not good idea to impute.
I believe that I can still do meta-analysis without standard deviation by calculating effect size (ES) with log response ratio (lnR) of means and later weight the studies using sample sizes as measure of variance.
I have also seen some studies that just defined the variance as I think the inverse of sample size, but I’m not sure how valid or accepted that is as a method.
Would lnR be the best solution? Anybody has done meta-analysis in this fashion or knows the best way to proceed?