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Article

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

by
Sixia Chen
1,*,
Alexandra May Woodruff
1,
Janis Campbell
1,
Sara Vesely
1,
Zheng Xu
2 and
Cuyler Snider
3
1
Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St., Oklahoma City, OK 73104, USA
2
Department of Mathematics and Statistics, Wright State University, Dayton, OH 45324, USA
3
Southern Plains Tribal Health Board, 9705 Broadway Ext, Oklahoma City, OK 73114, USA
*
Author to whom correspondence should be addressed.
Stats 2023, 6(2), 617-625; https://doi.org/10.3390/stats6020039
Submission received: 10 April 2023 / Revised: 29 April 2023 / Accepted: 5 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Advances in Probability Theory and Statistics)
Review Reports Versions Notes

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.
Keywords: nonprobability sample; multivariate imputation; public health data; selection bias nonprobability sample; multivariate imputation; public health data; selection bias

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MDPI and ACS Style

Chen, S.; Woodruff, A.M.; Campbell, J.; Vesely, S.; Xu, Z.; Snider, C. Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research. Stats 2023, 6, 617-625. https://doi.org/10.3390/stats6020039

AMA Style

Chen S, Woodruff AM, Campbell J, Vesely S, Xu Z, Snider C. Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research. Stats. 2023; 6(2):617-625. https://doi.org/10.3390/stats6020039

Chicago/Turabian Style

Chen, Sixia, Alexandra May Woodruff, Janis Campbell, Sara Vesely, Zheng Xu, and Cuyler Snider. 2023. "Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research" Stats 6, no. 2: 617-625. https://doi.org/10.3390/stats6020039

APA Style

Chen, S., Woodruff, A. M., Campbell, J., Vesely, S., Xu, Z., & Snider, C. (2023). Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research. Stats, 6(2), 617-625. https://doi.org/10.3390/stats6020039

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