I've got a dataset on agricultural trials. My response variable is a response ratio: log(treatment/control). I'm interested in what mediates the difference, so I'm running RE meta-regressions (unweighted, because is seems pretty clear that effect size is uncorrelated with variance of estimates).
Each study reports grain yield, biomass yield, or both. I can't impute grain yield from studies that report biomass yield alone, because not all of the plants studied were useful for grain (sugar cane is included, for instance). But each plant that produced grain also had biomass.
For missing covariates, I've been using iterative regression imputation (following Andrew Gelman's textbook chapter). It seems to give reasonable results, and the whole process is generally intuitive. Basically I predict missing values, and use those predicted values to predict missing values, and loop through each variable until each variable approximately converges (in distribution).
Is there any reason why I can't use the same process to impute missing outcome data? I can probably form a relatively informative imputation model for biomass response ratio given grain response ratio, crop type, and other covariates that I have. I'd then average the coefficients and VCV's, and add the MI correction as per standard practice.
But what do these coefficients measure when the outcomes themselves are imputed? Is the interpretation of the coefficients any different than standard MI for covariates? Thinking about it, I can't convince myself that this doesn't work, but I'm not really sure. Thoughts and suggestions for reading material are welcome.