  Often both Aggregate Data (AD) 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.
Moreover, often for some studies with AD, the associated IPD maybe available,
albeit at some extra effort or cost to the analyst. We propose a method for
combining treatment effects across trials when the response is from the
exponential family of distribution and hence a generalized linear model
structure can be used. We consider the case when treatment effects are fixed
and common across studies. Using the proposed combination method, we evaluate
the wisdom of choosing AD when IPD is available by studying the relative
efficiency of analyzing all IPD studies versus combining various percentages of
AD and IPD studies. For many different models design constraints under which
the AD estimators are the IPD estimators, and hence fully efficient, are known.
For such models we advocate a selection procedure that chooses AD studies over
IPD studies in a manner that force least departure from design constraints and
hence ensures a fully efficient combined AD and IPD estimator.
