Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared
Hieab H.H. Adams,
Lenore J. Launer,
Joshua C Bis,
Paul A Nyquist,
Jeroen Van der Grond,
Thomas H Mosley,
Gennady V. Roshchupkin,
Claudia L. Satizabal,
Lisa R. Yanek,
Sven J Van der Lee,
Konstantinos A Arfanakis,
James T Becker,
Wiro J. Niessen,
Anton JM de Craen,
Fabrice F Crivello,
Li An Lin,
Debra A Fleischman,
Tien Yin Wong,
Oscar H Franco,
J. Wouter Jukema,
Philip L. De Jager,
Bernard M Mazoyer,
David A. Bennett,
Ian J Deary,
M Kamran Ikram,
Hans J Grabe,
Cornelia M Van Duijn,
Meike W Vernooij,
M. Arfan Ikram,
on behalf of the HD-READY Consortium
Posted 07 Feb 2016
bioRxiv DOI: 10.1101/038893
Posted 07 Feb 2016
Joint analysis of data from multiple studies in collaborative efforts strengthens scientific evidence, with the gold standard approach being the pooling of individual participant data (IPD). However, sharing IPD often has legal, ethical, and logistic constraints for sensitive or high-dimensional data, such as in clinical trials, observational studies, and large-scale omics studies. Therefore, meta-analysis of study-level effect estimates is routinely done, but this compromises on statistical power, accuracy, and flexibility. Here we propose a novel meta-analytical approach, named partial derivatives meta-analysis, that is mathematically equivalent to using IPD, yet only requires the sharing of aggregate data. It not only yields identical results as pooled IPD analyses, but also allows post-hoc adjustments for covariates and stratification without the need for site-specific re-analysis. Thus, in case that IPD cannot be shared, partial derivatives meta-analysis still produces gold standard results, which can be used to better inform guidelines and policies on clinical practice.
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