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Quadratic inference function approach to merging longitudinal studies: validation and joint estimation

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  • Fei Wang
  • Lu Wang
  • Peter X.-K. Song

Abstract

Merging data from multiple studies has been widely adopted in biomedical research. In this paper, we consider two major issues related to merging longitudinal datasets. We first develop a rigorous hypothesis testing procedure to assess the validity of data merging, and then propose a flexible joint estimation procedure that enables us to analyse merged data and to account for different within-subject correlations and follow-up schedules in different studies. We establish large sample properties for the proposed procedures. We compare our method with meta analysis and generalized estimating equations and show that our test provides robust control of Type I error against both misspecification of working correlation structures and heterogeneous dispersion parameters. Our joint estimating procedure leads to an improvement in estimation efficiency on all regression coefficients after data merging is validated. Copyright 2012, Oxford University Press.

Suggested Citation

  • Fei Wang & Lu Wang & Peter X.-K. Song, 2012. "Quadratic inference function approach to merging longitudinal studies: validation and joint estimation," Biometrika, Biometrika Trust, vol. 99(3), pages 755-762.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:3:p:755-762
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    File URL: http://hdl.handle.net/10.1093/biomet/ass021
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    Cited by:

    1. Tian Gu & Jeremy Michael George Taylor & Bhramar Mukherjee, 2023. "A synthetic data integration framework to leverage external summary‐level information from heterogeneous populations," Biometrics, The International Biometric Society, vol. 79(4), pages 3831-3845, December.
    2. Hector, Emily C. & Luo, Lan & Song, Peter X.-K., 2023. "Parallel-and-stream accelerator for computationally fast supervised learning," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    3. Fei Wang & Lu Wang & Peter X.‐K. Song, 2016. "Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements," Biometrics, The International Biometric Society, vol. 72(4), pages 1184-1193, December.

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