@article {Adams038893, author = {Hieab HH Adams and Hadie Adams and Lenore J Launer and Sudha Seshadri and Reinhold Schmidt and Joshua C Bis and Stephanie Debette and Paul A Nyquist and Jeroen Van der Grond and Thomas H Mosley, Jr and Jingyun Yang and Alexander Teumer and Saima Hilal and Gennady V Roshchupkin and Joanna M Wardlaw and Claudia L Satizabal and Edith Hofer and Ganesh Chauhan and Albert Smith and Lisa R Yanek and Sven J Van der Lee and Stella Trompet and Vincent Chouraki and Konstantinos A Arfanakis and James T Becker and Wiro J Niessen and Anton JM de Craen and Fabrice F Crivello and Li An Lin and Debra A Fleischman and Tien Yin Wong and Oscar H Franco and Katharina Wittfeld and J Wouter Jukema and Philip L De Jager and Albert Hofman and Charles DeCarli and Dimitris Rizopoulos and WT Longstreth, Jr and Bernard M Mazoyer and Vilmundar Gudnason and David A Bennett and Ian J Deary and M Kamran Ikram and Hans J Grabe and Myriam Fornage and Cornelia M Van Duijn and Meike W Vernooij and M Arfan Ikram and on behalf of the HD-READY Consortium}, title = {Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared}, elocation-id = {038893}, year = {2016}, doi = {10.1101/038893}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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.}, URL = {https://www.biorxiv.org/content/early/2016/02/07/038893}, eprint = {https://www.biorxiv.org/content/early/2016/02/07/038893.full.pdf}, journal = {bioRxiv} }