Statistical Harmonization Methods in Individual Participants Data Meta-Analysis are Highly Needed

Meta-analysis can roughly be divided into aggregate data (AD) meta-analysis and individual participant data (IPD) meta-analysis. AD meta-analysis is the traditional form of meta-analysis and fully focuses on pooling effect sizes that are formulated at the study level, i.e. this analysis represents an analysis of analyses [6]. AD meta-analysis typically combines reported summary statistics from published articles. IPD meta-analysis considers the full data sets from the included studies and analyzes these data sets either as one large data set (one-stage IPD) or in two steps (two-stage IPD). In this two-stage approach the same statistical model is typically fitted to each study separately before the estimated summary statistics or effect sizes from these models are combined using methods from the AD meta-analysis. IPD meta-analysis is considered more appropriate than AD meta-analysis [7-10], in particular when observational studies are combined or pooled, since it provides the opportunity to correct for (the same) potential confounders at the individual level.