Missing Outcome Data in Epidemiologic Studies

• Published in: American journal of epidemiology Vol. 192; no. 1; pp. 6 - 10
• Main Authors: Cole, Stephen R, Zivich, Paul N, Edwards, Jessie K, Ross, Rachael K, Shook-Sa, Bonnie E, Price, Joan T., Stringer, Jeffrey S A
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 06.01.2023; Oxford Publishing Limited (England)
• Subjects: Bias; Clinical outcomes; Clinical trials; Data collection; Data Interpretation, Statistical; Epidemiologic Studies; Epidemiology; Humans; Medical research; Missing data; Randomized Controlled Trials as Topic; generalized computation; imputation; error; missing data; bias; precision
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwac179

Abstract Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.