Missing Outcome Data in Epidemiologic Studies

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Citation

Cole, Stephen R.; Zivich, Paul N.; Edwards, Jessie K.; Ross, Rachael; Shook-Sa, Bonnie E.; Price, Joan T.; & Stringer, Jeffrey S. A. (2023). Missing Outcome Data in Epidemiologic Studies. American Journal of Epidemiology, 192(1), 6-10. PMCID: PMC10144620

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 where outcomes are A) missing completely at random, B) missing at random with positivity, C) missing at random without positivity, and D) 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.

URL

http://dx.doi.org/10.1093/aje/kwac179

Reference Type

Journal Article

Year Published

2023

Journal Title

American Journal of Epidemiology

Author(s)

Cole, Stephen R.
Zivich, Paul N.
Edwards, Jessie K.
Ross, Rachael
Shook-Sa, Bonnie E.
Price, Joan T.
Stringer, Jeffrey S. A.

Article Type

Regular

PMCID

PMC10144620

Data Set/Study

Improving Pregnancy Outcomes with Progesterone (IPOP) Trial

Continent/Country

Zambia

Sex/Gender

Women

ORCiD

Zivich - 0000-0002-9932-1095
Edwards, J -0000-0002-1741-335X