Multivariate random-effects meta-analysis for sparse data using smvmeta

Multivariate meta-analysis is used to synthesize estimates of multiple quantities (“effect sizes”), such as risk factors or treatment effects, accounting for correlation and typically also heterogeneity. In the most general case, estimation can be intractable if data are sparse (for example, many risk factors but few studies) because the number of model parameters that must be estimated scales quadratically with the number of effect sizes. I will present a new meta-analysis model and Stata command, smvmeta, that make estimation tractable by modeling correlation and heterogeneity in a low-dimensional space via random projection and that provide more precise estimates than meta-regression (a reasonable alternative model that could be used when data are sparse). I will explain how to use smvmeta to analyze data from a recent meta-analysis of 23 risk factors for pain after total knee arthroplasty.