Comparing weighting and imputation methods for enhancing statistical inference of health surveys given administrative claims data
Seho Park, Brianna L Hardy, and A James O\'Malley

TL;DR
This paper evaluates methods to improve healthcare survey estimates by integrating administrative claims data through multiple imputation, comparing it to traditional weighting approaches to address biases and enhance inference accuracy.
Contribution
It introduces and compares multiple imputation strategies using administrative claims data to improve survey estimates of healthcare organizations, surpassing traditional weighting methods.
Findings
Imputation methods improve estimate accuracy over naive analysis.
Claims data integration reduces bias from non-response.
Method comparisons show advantages of proposed imputation strategies.
Abstract
National surveys of the healthcare system in the United States were conducted to characterize the structure of healthcare system and investigate the impact of evidence-based innovations in healthcare systems on healthcare services. Administrative data is additionally available to researchers raising the question of whether inferences about healthcare organizations based on the survey data can be enhanced by incorporating information from auxiliary data. Administrative data can provide information for dealing with under-coverage-bias and non-response in surveys and for capturing more sub-populations. In this study, we focus on the use of administrative claims data to improve estimates about means of survey items for the finite population. Auxiliary information from the claims data is incorporated using multiple imputation to impute values of non-responding or non-surveyed organizations.…
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Taxonomy
TopicsHealthcare Policy and Management · Patient Satisfaction in Healthcare · Primary Care and Health Outcomes
