Missing Outcome Data in Epidemiologic Studies

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.

Automated summary

Missing data is a common and significant problem in epidemiology, leading to decreased precision and notable bias. There are currently too few simple examples illustrating the types of missing data and their impact on results, and ignoring missing data remains a standard approach in epidemiology.