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Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies

Author

Listed:
  • Zhongzhe Ouyang

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Lu Wang

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Alzheimer’s Disease Neuroimaging Initiative

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
    Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu ). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf (accessed on 29 February 2024).)

Abstract

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer’s Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer’s Disease, which is crucial for early disease detection and personalized treatment.

Suggested Citation

  • Zhongzhe Ouyang & Lu Wang & Alzheimer’s Disease Neuroimaging Initiative, 2024. "Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies," Mathematics, MDPI, vol. 12(7), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:951-:d:1362481
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    References listed on IDEAS

    as
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