Often both aggregate or meta-analysis (MA) studies and Individual Patient Data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta-analytic estimates of treatment effects. We propose a method to combine treatment effects across trials where the response can be binary or continuous. For some studies with MA data, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We consider the case when treatment effects are random and evaluate the wisdom of choosing MA when IPD is available by studying the relative efficiency of analyzing all IPD studies versus combining various percentages of MA and IPD studies. For many different models design constraints under which the MA estimators are the IPD estimators, and hence fully efficient, are known. For such models we advocate a selection procedure that chooses MA studies over IPD studies in a manner that force least departure from design constraints and hence ensures a fully efficient combined MA and IPD estimator.