I am conducting an individual patient level data meta-analysis using the survey package in R (I tried Stata 13 but I get stuck with an error).
As per meta-analytic practice, I would prefer to conduct both fixed and random effect analyses, but it appears no such option is available with the survey packages in either Stata or R. Briefly, study-level meta-analysis are typically based on a fixed-effect approach (eg Peto) when there is limited statistical inconsistency/heterogeneity. Conversely, a random-effect approach (eg DerSimonian-Laird) is used when inconsistency/heterogeneity is significant or when a more robust (but potentially less sensitive) analysis is sought (see for instance Kelley and Kelley for a brief tutorial on study-level meta-analysis).
The approach to patient-level meta-analysis is less established. A common strategy is to conduct analysis in two phases (first analyzing each study as if it was alone), and then combining with study-level meta-analytic methods the study-specific results. This approach, called two-stage, is easily conducted and reasonably robust.
More recently, one-stage approaches have been advocated, in which patient-level modelling takes into account study-level clustering. Several methods have been proposed for this analytical strategy, from conditional regression to generalized linear models or generalized estimating equations (see for instance Burke et al).
I have recognized that the survey packages in Stata and R can perform clustered analysis easily, but I am not sure whether I can specify fixed vs random effect in such models, nor whether I can quantify statistical inconsistency/heterogeneity between studies within such model.
My assumption though is that the survey package provides analyses which are equivalent to those of a generalized linear model or generalizing estimating equations with fixed effects with explicit correlations acknowledge in the model.