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Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research

Author

Listed:
  • Sixia Chen

    (Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St., Oklahoma City, OK 73104, USA)

  • Alexandra May Woodruff

    (Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St., Oklahoma City, OK 73104, USA)

  • Janis Campbell

    (Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St., Oklahoma City, OK 73104, USA)

  • Sara Vesely

    (Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St., Oklahoma City, OK 73104, USA)

  • Zheng Xu

    (Department of Mathematics and Statistics, Wright State University, Dayton, OH 45324, USA)

  • Cuyler Snider

    (Southern Plains Tribal Health Board, 9705 Broadway Ext, Oklahoma City, OK 73114, USA)

Abstract

Nonprobability samples have been used frequently in practice including public health study, economics, education, and political polls. Naïve estimates based on nonprobability samples without any further adjustments may suffer from serious selection bias. Mass imputation has been shown to be effective in practice to improve the representativeness of nonprobability samples. It builds an imputation model based on nonprobability samples and generates imputed values for all units in the probability samples. In this paper, we compare two mass imputation approaches including latent joint multivariate normal model mass imputation (e.g., Generalized Efficient Regression-Based Imputation with Latent Processes (GERBIL)) and fully conditional specification (FCS) procedures for integrating multiple outcome variables simultaneously. The Monte Carlo simulation study shows the benefits of GERBIL and FCS with predictive mean matching in terms of balancing the Monte Carlo bias and variance. We further evaluate our proposed method by combining the information from Tribal Behavioral Risk Factor Surveillance System and Behavioral Risk Factor Surveillance System data files.

Suggested Citation

  • Sixia Chen & Alexandra May Woodruff & Janis Campbell & Sara Vesely & Zheng Xu & Cuyler Snider, 2023. "Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research," Stats, MDPI, vol. 6(2), pages 1-9, May.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:2:p:39-625:d:1141547
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    References listed on IDEAS

    as
    1. Jae Kwang Kim & Seho Park & Yilin Chen & Changbao Wu, 2021. "Combining non‐probability and probability survey samples through mass imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 941-963, July.
    2. Yilin Chen & Pengfei Li & Changbao Wu, 2020. "Doubly Robust Inference With Nonprobability Survey Samples," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2011-2021, December.
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