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On the relative efficiency of using summary statistics versus individual-level data in meta-analysis

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  • D. Y. Lin
  • D. Zeng

Abstract

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. Copyright 2010, Oxford University Press.

Suggested Citation

  • D. Y. Lin & D. Zeng, 2010. "On the relative efficiency of using summary statistics versus individual-level data in meta-analysis," Biometrika, Biometrika Trust, vol. 97(2), pages 321-332.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:2:p:321-332
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    File URL: http://hdl.handle.net/10.1093/biomet/asq006
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    Cited by:

    1. Elena Kulinskaya & Stephan Morgenthaler & Robert G. Staudte, 2014. "Combining Statistical Evidence," International Statistical Review, International Statistical Institute, vol. 82(2), pages 214-242, August.
    2. Changgee Chang & Zhiqi Bu & Qi Long, 2023. "CEDAR: communication efficient distributed analysis for regressions," Biometrics, The International Biometric Society, vol. 79(3), pages 2357-2369, September.
    3. Wei Wang & Shou‐En Lu & Jerry Q. Cheng & Minge Xie & John B. Kostis, 2022. "Multivariate survival analysis in big data: A divide‐and‐combine approach," Biometrics, The International Biometric Society, vol. 78(3), pages 852-866, September.
    4. Zhang, Hong & Wu, Zheyang, 2022. "The general goodness-of-fit tests for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    5. Jincheng Zhou & James S. Hodges & Haitao Chu, 2020. "Rejoinder to “CACE and meta‐analysis (letter to the editor)” by Stuart Baker," Biometrics, The International Biometric Society, vol. 76(4), pages 1385-1389, December.
    6. Ding‐Geng Chen & Dungang Liu & Xiaoyi Min & Heping Zhang, 2020. "Relative efficiency of using summary versus individual data in random‐effects meta‐analysis," Biometrics, The International Biometric Society, vol. 76(4), pages 1319-1329, December.
    7. Tang, Lu & Zhou, Ling & Song, Peter X.-K., 2020. "Distributed simultaneous inference in generalized linear models via confidence distribution," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    8. Hong Zhang & Zheyang Wu, 2023. "The generalized Fisher's combination and accurate p‐value calculation under dependence," Biometrics, The International Biometric Society, vol. 79(2), pages 1159-1172, June.
    9. Dungang Liu & Regina Y. Liu & Minge Xie, 2015. "Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 326-340, March.
    10. Jiang, Rong & Yu, Keming, 2020. "Single-index composite quantile regression for massive data," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    11. Nicole Deflaux & Margaret Sunitha Selvaraj & Henry Robert Condon & Kelsey Mayo & Sara Haidermota & Melissa A. Basford & Chris Lunt & Anthony A. Philippakis & Dan M. Roden & Joshua C. Denny & Anjene Mu, 2023. "Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    12. Li, Haoqi & Lin, Huazhen & Yip, Paul S.F. & Li, Yuan, 2019. "Estimating population size of heterogeneous populations with large data sets and a large number of parameters," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 34-44.

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