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Activity Number: 311 - Does Missing Data Affect Outcomes Examined Using Nationally Representative Survey Databases? A Comparison of Traditional and Data Science Approaches
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #320407
Title: A Review of Missing Data Approaches and Practices in Large Nationally Representative Survey Databases
Author(s): Parul Agarwal*
Companies: Icahn School of Medicine at Mount Sinai
Keywords: missing data; survey data; data science; multiple imputation; large databases; research methods
Abstract:

Large nationally representative survey databases are frequently used for clinical evidence generation. These databases often have missing data. A common approach is to delete cases with missing data which may affect the association between treatment and outcome. In this paper the following is discussed: 1) missing data issues and insights to analyze it with both traditional and data science approaches, 2) a systematic review of methods and missing data reporting practices in large national survey databases, and 3) examples where missing data may potentially affect the association between treatment and outcome. Our findings suggest that the practices and methods used in published literature varied and highlight a need for transparent reporting of findings.


Authors who are presenting talks have a * after their name.

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