|Title||On the relative efficiency of using summary statistics versus individual-level data in meta-analysis.|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Lin, D Y., and D Zeng|
|Date Published||2010 Jun|
Meta-analysis is widely used to synthesize the results of multiple 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. In the present paper, we investigate the relative efficiency of analyzing original data versus combining summary statistics. We show that, for all commonly used parametric and semiparametric models, there is no asymptotic 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. We also assess the relative efficiency of the two methods when the parameter of main interest has different values among studies or when there are common nuisance parameters across studies. We conduct simulation studies to confirm the theoretical results and provide empirical comparisons from a genetic association study.
|Original Publication||On the relative efficiency of using summary statistics versus individual-level data in meta-analysis.|
|PubMed Central ID||PMC3412575|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
On the relative efficiency of using summary statistics versus individual-level data in meta-analysis.