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Sib-pair genetic longitudinal studies with missing not at random data

  • Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this: JUSTC, 2024, 54(12): 1203
https://doi.org/10.52396/JUSTC-2024-0026
CSTR: 32290.14.JUSTC-2024-0026
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  • Author Bio:

    Siyu Jiang is currently a graduate student under the tutelage of Prof. Hong Zhang at the University of Science and Technology of China. His research mainly focuses on statistical genetics

    Hong Zhang is a Full Professor at the University of Science and Technology of China (USTC). He received his Bachelor’s degree in Mathematics and Ph.D. degree in Statistics from USTC in 1997 and 2003, respectively. His research mainly focuses on statistical genetics, causal inference, and machine learning

  • Corresponding author:

    Hong Zhang, E-mail: zhangh@ustc.edu.cn

  • Received Date: February 23, 2024
  • Accepted Date: April 24, 2024
    Abstract

  • In the interdisciplinary realm of statistics, genetics, and epidemiology, longitudinal sibling pair data offers a unique perspective for investigating complex diseases and traits, allowing the exploration of the dynamic processes of gene expression over time by controlling numerous confounding factors. Missing-not-at-random (MNAR) data are commonly used in such types of studies, but no statistical methods specifically tailored have been developed to handle MNAR data in complex longitudinal data in the literature. Here, we propose a new statistical method by jointly modeling longitudinal data from sib-pairs and MNAR data. Extensive simulations demonstrate the excellent finite sample properties of the proposed method.

    H-GEE method flowchart.

    Public Summary

    • The challenge in longitudinal studies lies in effectively addressing missing-not-at-random (MNAR) data, which complicates data analysis.
    • The proposed H-GEE method, combining the Heckman model and generalized estimating equations (GEE), aims to overcome MNAR challenges for robust data analysis.
    • Extensive simulations validate H-GEE’s effectiveness in handling MNAR data, highlighting its potential for advancing genetic and epidemiological research.
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