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 …

The article by Cole et al.(1) is a clear and straightforward demonstration of the impact of
missing outcome data. The authors use complete data from the Improving Pregnancy
Outcomes With Progesterone trial as a reference against which they compare 4 different,
induced missingdata scenarios. They demonstrate that using g-computation to account for
missing outcome data will alleviate bias and improve precision in most scenarios. By fitting
the outcome model within each treatment, gcomputation avoids making the homogeneity …