On The Equivalence Of Using Summary Statistics Versus Individual Level Data In Meta-analysis

Meta-analysis is a method of synthesizing the results of independent studies. Although meta-analysis is traditionally carried out by combining the summary statistics of relevant studies, advances in technologies and communications have made it increasingly feasible to access the original data on individual participants. However the distinction between analyses based on information extracted from the published literature and those based on collecting updated individual participants data is not clear. In the present paper, we investigate the relative efficiency of analyzing original data versus combining summary statistics. We show there is no efficiency gain by analyzing original data if the parameter of main interest has a common value across studies, the nuisance parameters have distinct values among studies, and the summary statistics are based on maximum likelihood for all commonly used parametric and semi-parametric models.